Ensures.

That is the entire piece in one word. The architecture of large language models is, by design, the opposite of ensured. And schema has nothing to do with whether an LLM can parse text. LLMs parse text by reading text.

Semrush is far from alone. Every SaaS vendor with skin in this game is running variations of the same play. SEO-era controllability, repackaged under a new acronym. The same percentages, pillars, and pyramids. All dressed for a system that was built specifically not to work this way.

I have made the strategic version of this case before, in Your AI Strategy Isn’t a Strategy. This piece is the technical floor underneath it.

Built to read whatever’s there

Language models exist because the web is a mess. Forums, Wikipedia stubs, blog posts written at 2am, scraped product copy, machine-translated junk, code comments, half-formed sentences, typos, contradictions, every register from journal article to subreddit shitpost. Pre-training data is the public web, and the public web has never been structured.

The transformer architecture handles this by treating language as sequences of tokens. There is no parser inside the model looking for <schema> tags. There is no preference for FAQ markup. The model reads the words. That is the mechanism.

At inference time, the model generates more tokens conditioned on the input. None of that pipeline is reading microdata.

Schema.org has real jobs. It feeds rich results in classical search. It supports entity disambiguation in the Knowledge Graph. It helps voice assistants pull structured fields. These are well-defined functions inside specific systems. They are not the mechanism by which an LLM understands a sentence.

So when a vendor claims structured data “ensures AI engines can parse and connect your content,” there is nothing to ensure. The parsing layer they are imagining is not there. The model already parsed your sentence. It did so by reading the sentence.

One trick, three brand colours

Look at the biggest GEO and AEO explainers in market right now and you find the same SEO-era playbook with the acronym swapped.

Semrush is already covered. The fourth pillar of their “Technical GEO” presents schema and structured data as ensuring something that the architecture cannot ensure.

AirOps published a graphic titled “15 Ways to Get Cited by ChatGPT, Perplexity, & Google.” It is the most numbers-heavy specimen of the genre I have seen this year. Schema markup increases citation likelihood by 13 percent. Sequential H2 to H4 tags double your chances. Short paragraphs make content 49 percent more likely to appear in AI answers. Perplexity cites UGC in 91 percent of answers, versus Gemini’s 7. Read the source notes and the methodology trail comes home. The numbers in the graphic trace back to AirOps’s own 2026 State of AI Search Report. AirOps is citing AirOps on the question of whether AirOps’s prescriptions work.

Peec AI does a more honest job in places. Its complete guide to GEO acknowledges the probabilistic nature of the system and concedes that foundation models are already trained, so optimisation focuses on the retrieval layer. Then it lands the same prescriptions: heading hierarchy, bullet lists, FAQ markup, multiple schema types layered on each page, summaries at the top of sections — all built on the chunking claim that long paragraphs lose out because the engine extracts fragments rather than full articles.

Profound, citing Aleyda Solis’s checklist, is the most explicit in their piece: “Optimize for Chunk-Level Retrieval.” Each section, a standalone snippet. Each page, a buffet from which the engine takes what it wants. The engine, in this telling, is a polite guest who only takes what’s been laid out.

Three vendors. Same operating assumption: a controllable, prescriptive technical discipline sits between a publisher and a citation, and it occupies roughly the same shape as classical SEO. Schema, headings, structure, freshness, machine-readable formats. Familiar. Billable. Reportable up to a CMO.

What schema actually does

Schema is not the target here. Schema has real, well-defined uses. Classical Google search uses it for rich results: prices, ratings, event times, the structured fields that drive SERP features. The Knowledge Graph uses it for entity disambiguation. Voice assistants pull structured fields out of it.

None of that goes away. If you’re responsible for technical SEO, keep implementing schema where it earns its keep.

Schema cannot reach into a transformer and improve its comprehension of your prose. The model isn’t architected to read schema as schema. It receives whatever text the engine fetched and chose to include, and processes that text as language tokens. The entire GEO/AEO marketing layer rests on conflating two distinct claims: that schema is useful in classical search, and that schema feeds the LLM. The first is true. The second is a category error.

Chunking is not yours to optimise

The chunking advice keeps reappearing because it sounds technical, sits neatly inside a flowchart, and gives a content team something concrete to do on Monday morning. It is also incoherent.

Chunking happens at retrieval time. Perplexity, ChatGPT, and Gemini each run a retriever over candidate documents, split them according to their own configurations (length, overlap, embedding model, sometimes semantic boundaries), and feed the top-k chunks into the model’s context. Those configurations belong to the engine. They get tuned differently across systems and retuned on schedules no publisher is privy to. The publisher’s view of the chunker is the publisher’s view of the model: black box, results only.

So when a vendor says “optimise for chunk-level retrieval,” what is actually being recommended is good writing. Short, self-contained paragraphs. Clear definitions near the top of sections. Internal logical structure. These are recognisable disciplines: information architecture, technical writing, readability. They have been recognisable disciplines since long before the transformer was invented. They are not a new technical layer.

A more honest version of the pitch would be: hire someone competent at writing for the web. That sentence does not fit on a pricing page.

The paper they don’t read

There is an actual academic paper called “GEO.” Aggarwal and co-authors, KDD 2024. It is the closest thing to a citable source the SaaS layer has when it sells Generative Engine Optimization as a discipline. It is also, as papers go, easy to skim. Nine “optimisation methods” are tested on a 10,000-query benchmark, with results.

What did the paper find worked?

Adding citations from credible sources. Adding quotations from relevant sources. Adding statistics. Improving fluency. Making prose easier to understand. The methods that produced the largest visibility lifts were essentially: write content with more evidence in cleaner prose.

What did the paper test and find did not work?

Keyword stuffing, the closest analogue in the paper to the SEO-era playbook the current GEO and AEO vendors have repackaged. Result: below baseline. The paper’s authors note in plain terms that techniques effective in search engines “may not translate to success in this new paradigm.”

Notice what is not in the list of nine methods. Schema. Structured data. FAQ markup. Heading hierarchy. Machine-readable formats. None of these are tested in the paper, because none of them are the optimisation surface the paper studies. The paper is studying content-level interventions: what you put in the words, not metadata layered around the words.

The SaaS layer borrowed the acronym. The findings stayed in the paper. “Technical GEO” is the SEO playbook with different stickers on the same boxes, sold against research that points the other way.

The assumption smuggled in

The SaaS pitch only makes sense if you smuggle in one assumption: that the system you’re optimising for has the same shape as the one that’s been billing SEO clients for a quarter-century. Inputs you control. Outputs that respond. A retrievable causal chain between the two.

That model was always a simplification of how search worked. It was close enough to keep the industry running, and close enough to keep the invoices going out.

None of that simplification survives contact with generative systems. The same prompt produces different answers across sessions, users, temperatures, model versions, and days. Observed behaviour across the major engines, not a clean property of any single one. The retrieval layer in front of the model also moves: candidate sources shift, ranking shifts, freshness windows shift. No causal chain runs between “I added FAQ schema” and “the model cited my page.” What runs between them is a probability distribution, and the things you control affect that distribution in ways nobody can cleanly attribute. Not even the people who created these systems.

This is the established line on AI visibility tools, repeated here because it applies to the whole prescriptive layer. Statistically unverifiable data drawn from non-deterministic systems. A 13 percent citation lift, measured how, against what counterfactual, with what reproducibility? The methodological questions aren’t what those numbers are designed to answer. The numbers are the answer. They land in a graphic, get rendered as ROI in a board deck, and the conversation moves on.

Something to say in the meeting

Here is the part that the architecture argument and the methodology argument do not, on their own, explain. Why does the entire SaaS layer keep successfully selling this stuff to people who are not stupid?

The honest version of the answer goes something like: we are operating with reduced visibility into a system that does not expose its mechanics, that returns different outputs to different people for the same query, that is changing month by month, and that has folded a substantial chunk of the funnel into a black box. We can keep doing the work that has always been the work: writing well, being useful, building authority, maintaining the site. We can monitor what shows up where. The deterministic dashboard we used to have is not coming back.

That sentence is unsayable in a marketing meeting. It admits the lever is not connected. It tells leadership that the budget line they approved does not have a corresponding action. It gives the team nothing to put in next quarter’s plan.

So the SaaS layer fills the gap. It manufactures levers. Pillars, frameworks, percentage lifts, schema audits, chunking optimisation, machine-readable formats. Reportable activity. Defensible expenditure. Something to say in the meeting. None of this gets you visibility. The engine decides that. What is on offer is the appearance of control, sold to people who would rather pay than concede that control left the room.

Once the lever is bought, it has to be operated. Schema audits get scheduled. Chunking checklists get reviewed. Citation likelihoods get tracked, refreshed, and compared. The dashboard the team paid for becomes the dashboard the team optimises against, and the dashboard quietly replaces the actual problem with the part of the problem it can see. By the time anyone notices, the SaaS layer is writing the brief.

None of this is a moral failure on the buyer’s side. What you are watching is what happens when an industry has been organised for a quarter-century around the premise that you can pull a lever and watch the meter move, and the meter quietly disconnects from the lever. The vendors aren’t running a con. They are filling demand for the only thing the buyer can no longer afford to do without: an answer that fits in a slide.

Rank and tank, all over again

I keep coming back to a phrase that fits this whole moment: dancing to the rank-and-tank tunes (I borrowed it from David McSweeney). The cycle goes: vendor sells the controllable-discipline frame, agencies adopt it, content teams scale production around the prescriptions, AI-generated articles get pumped out at volume because the prescriptions are easy to template. Some of it ranks for a while. Most of it eventually tanks because the prescriptions were never the mechanism, and the engine adjusts, or the freshness window closes, or the system simply moves on.

The SEO industry has done this before. Spinning. Mass programmatic pages. Doorway content. Each cycle followed the same shape: a controllable input dressed as a discipline, sold at scale, briefly effective, eventually punished by the engine, replaced by the next controllable input dressed as a discipline.

GEO and AEO are the current cycle. The pillars and percentages and pyramids are this cycle’s templates. Underneath them, the strategies bifurcate.

One path is brand presence exploitation. Plant your name where the engines look. Reddit threads, top-X listicles, the same citation surfaces over and over. The cycle feeds itself: engines cite the surfaces, brands work the surfaces, surfaces feed the engines. I have written about this loop before; I called it the Ouroboros pattern. The short version is that the loop is less stable than the strategy assumes.

The other path is content at scale. Produce variations, pump out volume, treat the templated output as content that could earn a citation. I have written about this approach before, in the Scaling Disappointment piece. The short version is that uniqueness is not value, and at the pace these prescriptions enable, qualitative review stops being possible. The volume of AI-generated copy produced under this path is this cycle’s externality.

The next cycle will sell the cleanup.

Forget for a second whether your “Technical GEO” is set up correctly. Ask whether the thing you are putting on the page is worth reading. Large language models were designed to read whatever is there. If what is there is good, it will be read. If what is there is templated, low-utility content optimised against a chunking heuristic that does not exist, it will eventually be filtered out: by the engine, by the user, or by the next academic paper showing that retrieval quality is degraded by exactly this kind of slop.

The advantage, when it accrues, will accrue to the people who do not get distracted. Who do not subscribe to the dashboard. Who keep working on product-driven SEO and the foundations that have always connected content to people. There are early signs of this on the timelines I read. Practitioners openly questioning whether optimising against a non-deterministic surface makes sense at all, and asking whether their attention belongs back on classical search; which, at the end of the chain, is what feeds these systems anyway.

The mess was always the point. The architecture handles it. The industry just needs to stop pretending the mess is the problem.

The migration shipped on Friday. The redirects went in. Someone on Monday noticed organic traffic had fallen off a cliff, and someone else remembered there was, in fact, an SEO on the team somewhere, and perhaps they should take a look.

This isn’t the meeting anyone puts on the LinkedIn headline. It’s the forensic cleanup. The polite explanation of why a decision you weren’t consulted on is now producing consequences you would have flagged if anyone had thought to tell you it was happening. The quiet realisation that your job, not for the first time, is to do archaeology on an outcome that was predictable the moment the roadmap got drafted without you in the room.

Welcome to SEO. The dark art. The mysterious discipline. The thing executives describe in tones usually reserved for alternative medicine and aggressive tax planning, and the profession The Verge once called “the people who ruined the internet.”

The industry has spent years worrying about this reputation. There are conference talks. There are working groups. There are, I assume, strategic initiatives. The prescribed fix never changes: SEOs need to communicate better. Get a seat at the table. Build executive presence. Translate technical concepts into business value. Develop soft skills. Take an MBA. Learn storytelling. The recommendations multiply every year, nobody moves, and everyone agrees there’s a problem.

Here is the part nobody at the working group seems willing to say: The reputation is earned. Not because SEO is actually mystical. It isn’t. The reputation is earned because the profession has been filed under marketing for twenty years, and a discipline responsible for outcomes it has no authority to produce behaves, from the outside, exactly like dark magic.

SEO is in the wrong part of the org chart. Everything called a perception problem flows from that one structural mistake. The rest of this post is what the mistake has produced, why it’s about to get materially worse, and why the people with the authority to fix it aren’t going to.

Responsibility without authority

Here’s the list of things SEO is expected to produce outcomes on.

URL structure. Rendering behaviour. Canonical signals. Internal linking architecture. Schema and structured data. Content modelling. Information architecture. Pagination. Faceted navigation. Crawl efficiency. Indexability logic. Status codes. Redirect chains. Site performance. Mobile parity. Image and media handling. Hreflang. The sitemaps. The robots directives. The rendering pipeline that decides whether any of the above reaches a crawler in the first place.

Here’s the list of those things marketing owns.

None of them.

Every item on the list is owned by product or engineering. Some of them are owned so deep inside engineering that getting them changed requires a ticket, a sprint, a roadmap slot, and a PM willing to argue for prioritising work they don’t personally benefit from shipping. That’s fine if you’re inside the org that owns the thing. It’s a problem if you’re a marketing function being asked to influence it.

This is the diagnosis. It has a name in every discipline that’s had to deal with it: responsibility without authority. Someone is accountable for an outcome they can’t unilaterally produce. It’s a known failure mode in organisational design, which is a polite way of saying every other discipline figured this out decades ago. In SEO, we’ve normalised it so thoroughly that practitioners build whole careers around being good at it.

The skill the industry calls “stakeholder management” is the skill of working around this problem. Translate. Influence. Build relationships. Earn trust. Tell the story. Take the executive to lunch. All of it is the language of a function that has to beg for every deliverable, because it can’t ship any of them itself.

Let’s be clear about what begging produces. When SEO needs a canonical fix, it asks engineering. When it needs a URL structure change, it asks engineering. When it needs schema implemented, it asks engineering. When it needs pagination logic redesigned, it asks engineering. When it needs the rendering pipeline adjusted so that the content actually reaches crawlers, it asks engineering very politely, ideally with a deck. None of those requests are guaranteed to be honoured. The function making the request doesn’t own the backlog, doesn’t set priorities, and doesn’t report into a leadership chain that can force the issue.

What does the function own? Content. Specifically, the content it can produce without asking anyone. That’s the one lever it can pull cleanly. We’ll come back to what that does to the profession’s theory of the world.

The genuinely embarrassing part is that everyone involved agrees the arrangement is dysfunctional. Marketing leaders complain that SEO can’t deliver. SEOs complain that they have no authority. Engineering complains that SEO requests arrive late and without context. Product complains that nobody told them. Everyone’s right. They’re all looking at the same structural error from different sides of it.

The fix isn’t better stakeholder management. The fix is moving the function to the part of the org that owns the surface area it’s responsible for. Not a title change. Not a dotted line. Not a cross-functional working group that meets on Thursdays. Actual reporting into the org that owns the work, with the authority, budget, and priority access that comes with it.

This isn’t a radical proposal. Every other technical function figured it out decades ago. Security used to be treated as a side concern of IT, was consistently marginalised, and now lives under its own leadership because the failure mode was too expensive to ignore. Site Reliability Engineering was invented specifically because treating reliability as someone’s part-time concern produced predictable outages at scale. Both disciplines moved when it became clear the work couldn’t be done from where it had been filed.

SEO is the last major technical discipline still filed under the function that doesn’t own any of the work.

Campaign time vs. infrastructure time

Marketing operates on campaign time. Quarterly planning. Monthly reporting. Weekly standups where someone asks what moved since last week. It’s a rhythm optimised for activity you can start, finish, and measure inside a budget cycle. That rhythm is load-bearing for a lot of what marketing actually does. Campaigns have start dates and end dates. Launches have moments. Brand work compounds, but most of the artefacts a CMO is evaluated on are discrete, time-boxed, and attributable to a quarter.

SEO doesn’t operate on campaign time. It operates on infrastructure time.

The work that produces durable search presence is the work of getting architectural decisions right and then leaving them alone for years. A URL structure that survives three migrations is worth more than a content campaign that trends for a week. An information architecture that scales with the business is worth more than any individual piece of content that lives inside it. The compounding is in the foundations, not the surface. Practitioners know this. It’s in every conference talk about technical debt, every post-mortem on a botched migration, every hushed conversation about a client who redesigned their site without telling anyone.

Try explaining infrastructure time to a function measured on quarterly pipeline contribution.

The function can’t hear it. Not because the people in it are stupid. They aren’t. It can’t hear it because the evaluation framework can’t process it. A CMO who invests this quarter’s budget in work that won’t produce visible returns for eighteen months is a CMO who won’t be at the company in eighteen months to see the returns. The incentive structure of the role forecloses the investment. You can’t ask someone to spend their political capital on outcomes that will land after they’ve been replaced. They will, rationally, spend it on outcomes that land before the next board review.

So the work gets compressed. The infrastructure conversation becomes a content conversation, because content can be produced inside a quarter. The migration becomes a launch, because launches have dates. The site architecture becomes a “content strategy refresh,” because that’s the vocabulary the budget line expects. Every part of SEO that doesn’t fit inside the campaign calendar either gets deprioritised, rebranded as something that does fit, or quietly declared out of scope.

This is why SEO audits sit on shelves. The audit correctly identifies forty-seven architectural issues that will take eighteen months of engineering time to resolve. The function responsible for acting on the audit has to report results next quarter. The maths doesn’t work. The audit gets filed, the function ships a content campaign, and the forty-seven issues stay on the shelf until the next agency writes the next audit identifying the same forty-seven issues, phrased slightly differently. Sometimes the new agency charges more for identifying them a second time. It’s a growth industry.

You can spot a site that’s been through this cycle a few times. It has a lot of content. It has very little that works.

The point isn’t that campaign time is wrong. It’s that campaign time and infrastructure time are different timescales, and you can’t get infrastructure outcomes by running infrastructure work on a campaign cadence. It’s the operational equivalent of asking someone to build a foundation during a sprint. You’ll get something poured, and it will not hold.

Product orgs already understand this. Roadmaps are measured in quarters but planned in years. Architectural decisions get scoped against multi-year consequences as a matter of routine. The entire discipline of engineering management exists to reconcile the short-term delivery pressure with the long-term integrity of the system. It’s not perfect. Product orgs have their own pathologies, and anyone who’s watched a feature factory run itself into the ground can list them. But the conceptual vocabulary for treating some work as infrastructural is there. The meetings exist. The rituals exist. The career paths exist for people whose job is to protect long-horizon work from short-horizon pressure.

None of that vocabulary exists in marketing. It was never supposed to. Marketing’s job isn’t infrastructure. That’s the point. Putting SEO in marketing isn’t just a filing error. It’s filing infrastructure work inside the one function in the org explicitly structured not to do infrastructure work.

When content is your only lever

Here is the thing about being trapped in responsibility without authority on campaign time: it shapes what you believe.

A function can only develop expertise in the problems it’s allowed to solve. When the only lever a profession can pull without asking permission is content, the profession slowly, over years, over careers, over decades, develops a theory of the world in which content is the answer to most problems. Not because anyone sat down and decided that. Because the feedback loops that produce professional instincts only have one surface to operate on.

You ship content. You measure content. You get promoted for content. You train juniors on content. You write conference talks about content. You consult on content. Eventually you believe, in the genuine, felt-in-your-bones way that professional instinct works, that content is what SEO is. Not because you’ve reasoned your way there. Because it’s the only thing the structure around you has ever let you ship.

Google hasn’t exactly helped. For twenty years, the headline advice out of Mountain View has been “create great content.” Never “build a valuable product.” Never “get your architecture right.” Never “think about whether the thing you’re publishing should exist.” The technical guidance is in the docs, buried where most of the profession doesn’t read, written for engineers who aren’t the ones being asked to act on it. The loud advice, the one that became the industry’s shared vocabulary, the one repeated in every Search Central video and quoted in every agency pitch, was always about content. Practitioners followed the loudest signal available. That’s what professionals do. The signal pointed at content for two decades, and the profession dutifully pointed itself in the same direction.

None of this absolves the industry. It does explain why the dysfunction has been so stable for so long. When the structure you work inside can only fund content, and the authority figure you’re trying to please only talks about content, developing a non-content theory of the work requires actively ignoring two reinforcing signals at once. Most people don’t. Most people shouldn’t have to.

This is how the industry ends up with GEO positioned as “optimise your content for LLMs”. It’s how “E-E-A-T” got absorbed as a content-quality checklist rather than what it actually describes. It’s how every major architectural shift in search and retrieval over the last five years has been immediately translated into a content prescription, regardless of whether the underlying change had anything to do with content.

AI Overviews arrive. The industry response: write content that gets cited.

RAG pipelines become load-bearing for half the AI assistants on the internet. The industry response: write content that chunks well.

Agent browsers start navigating sites on behalf of users. The industry response, presumably arriving next quarter: write content for agents.

None of these responses are wrong, exactly. Content does play a role. The response is incomplete in a consistent, predictable direction, and the direction is the direction of the only tool the profession has ever been allowed to use. When all you have is a content calendar, everything looks like a content gap.

The architectural work that actually governs whether any of this succeeds (rendering pipelines, data models, URL design, schema, crawl surfaces, API exposure, the question of whether your application even produces stable, parseable HTML for a non-human client) sits outside the function making the prescription. So the prescription routes around it. It has to. The function doesn’t have the authority to ship the architectural work, so the architectural work exits the recommendation.

What the client receives is a content strategy. What the client needed was an architecture review. The consultant isn’t being dishonest. They’re producing the output their seat in the org allows them to produce.

This is the dysfunction loop. Marketing placement produces a content-only toolkit. The content-only toolkit produces a content-only theory of the work. The content-only theory produces prescriptions that ignore architecture. Sites spend a decade following those prescriptions. The architectural debt compounds. Eventually something important breaks, a migration, a rendering change, a platform shift, and someone wonders why all that content didn’t save them.

It couldn’t have. The content was never the load-bearing layer. The industry just lost the vocabulary to describe what was.

Four retrieval contexts, one architecture problem

For most of SEO’s existence, there was one retrieval system that mattered. Googlebot crawled, Google’s index stored, Google’s ranking algorithms ordered, and whatever Google shipped defined the work. The profession developed against a single target. That target’s requirements were stable enough, for long enough, that “SEO” and “Google SEO” became synonymous to the point where nobody noticed the conflation.

That era is over. The target has multiplied, and the new targets don’t share requirements.

My read is that most sites now need to be correctly interpreted by not one retrieval system but at least four, each with materially different architectural needs. Classic search crawlers still do what they’ve always done, fetch, render, index, rank, and still care about rendered HTML, canonical signals, internal linking, and crawl efficiency. That work hasn’t disappeared. It’s just no longer the only work.

Alongside it sit three classes of retrieval that have arrived, are arriving, or are about to arrive at scale. Retrieval-augmented generation pipelines, where content gets chunked, embedded, and surfaced in response to queries that may never touch a traditional search results page. Agent browsers, where software acts on behalf of a user, filling forms, completing purchases, extracting information, and needs the site’s interactive surface to be stable, parseable, and actionable. Training crawlers, whose presence on your site determines whether your content is even available to the models that power everything upstream of it.

Each of these has different requirements, or at least that’s what the behaviour I’ve seen suggests. RAG pipelines appear to reward content that survives chunking: semantic boundaries that align with how retrieval windows get constructed, prose dense enough to carry meaning when it’s separated from its surroundings, metadata that persists through tokenisation. Agent browsers appear to reward interactive affordances that hold up to automation: stable selectors, predictable navigation, structured data that describes what can be done on a page and not just what it says. Classic crawlers reward what they’ve always rewarded, and the requirements occasionally conflict with the others. Content optimised for chunking may render poorly when the crawler expects traditional page structure, and sites that expose rich agent-friendly surfaces may produce indexing decisions that weren’t obvious a year ago.

I’d stake the general shape of this on the table: multiple retrieval contexts with overlapping but non-identical requirements. The specifics will keep moving.

None of this is a content problem. All of it is an architecture problem.

Deciding how your site serves four classes of non-human client with partially conflicting requirements is a product decision. It touches the rendering pipeline, the data model, the API surface, the URL structure, the caching strategy, the authentication boundaries, the robots directives. It requires trade-offs that can only be made by someone with the authority to ship across engineering and content at the same time. It requires someone to look at the four retrieval contexts, understand which ones matter for this specific business, and accept that you probably can’t optimise for all of them simultaneously.

That person is not in the marketing meeting. That person is in the architecture meeting, which is a meeting SEO generally isn’t invited to.

So what’s happening instead is predictable and, at this point, depressing to watch. The industry is translating each new retrieval context into a content prescription, because that’s the translation its org chart permits. RAG arrives and the advice is “structure your content for retrieval.” Agent browsers arrive and the advice will be, within a quarter or two, “write content agents can parse.” The architectural half of the work, the half that determines whether any of the content prescriptions can even be executed at the infrastructure level, goes unsaid, because the people producing the advice don’t have authority over it.

Meanwhile the product teams making the actual architectural decisions aren’t making them with any of this in mind. They’re not malicious. They’re not negligent. They’re just not briefed, because the function that should be briefing them is in a different part of the org, producing a content strategy instead.

This is how sites go dark. Not in a single dramatic event. In a compounding sequence of architectural decisions, each individually defensible, made without anyone in the room whose job is to notice what happens at the retrieval layer when you ship them all together. The site doesn’t disappear. Its presence across four retrieval systems gets quietly, progressively worse, and by the time it shows up in a dashboard it’s already eighteen months of decisions too late to fix cleanly.

The function that could have prevented this was downstairs, writing a blog post about E-E-A-T.

Decisions in rooms SEO isn’t in

Ask any technical SEO with a decade of scar tissue to describe the worst projects of their career. You’ll get a specific kind of story.

Someone decided to migrate the CMS. Someone decided to rebuild the frontend in a framework that rendered client-side by default. Someone decided a new URL structure was cleaner. Someone decided the old blog wasn’t worth porting. Someone decided the redirect logic could be simplified. Someone decided canonical tags were “technical debt.” Someone decided that consolidating three subdomains into one would be straightforward. Someone decided product pages didn’t need structured data because the new design handled it visually.

The word “someone” is doing a lot of work in those sentences. The someone is never an SEO. The SEO arrives after the decision has shipped, gets asked to “help with the SEO implications,” and spends the next six to eighteen months producing a recovery plan for a catastrophe that was entirely predictable at the point the decision got made.

This is not an accident of scheduling. It’s a structural consequence of where the function sits. Architectural decisions get made in meetings that belong to product and engineering. Those meetings have invite lists. The invite lists reflect who owns the work and who has the authority to block it. SEO, filed under marketing, is on neither list. Sometimes a senior SEO in a mature org has cultivated enough relationships to hear about decisions informally, early enough to influence them. That’s an individual achievement won in spite of the structure, not a property of the structure itself. The default state, the thing that happens when nobody has engineered around the problem, is that the architectural decisions get made and SEO finds out afterwards.

“Get SEO a seat at the table” is the industry’s perennial rallying cry for this problem. Twenty years of conference talks have urged practitioners to be more strategic, more influential, more business-aligned, more whatever it takes to get invited to the meeting. It hasn’t worked. It can’t work. The meeting isn’t a table you get invited to. It’s a meeting for the org that owns the system being decided on. You’re either in that org or you’re not, and no amount of storytelling improves the address on your org chart.

The practitioners who have, genuinely, managed to get into the room did it the hard way. They built enough credibility with product and engineering leadership that they became de facto members of decisions they had no de jure claim on. That’s a real achievement. It’s also not reproducible at scale. A profession can’t be built on the assumption that every practitioner will, through force of personality and political skill, overcome the structural arrangement of the function. Most won’t. Most shouldn’t have to.

The fix is to change the arrangement, not to keep demanding that individual practitioners heroically overcome it.

The field isn’t what it looks like from the outside

There’s a second-order problem here that the “move SEO to product” argument has to confront honestly. Even if the org chart changed tomorrow, the supply of practitioners who could do the work the new placement demands is thinner than the industry is willing to admit.

SEO has the lowest barrier to entry of any technical discipline in the digital field. No certification actually means anything. No degree is required. No apprenticeship is standardised. People arrive from copywriting, from PPC, from affiliate marketing, from blogging, from whatever adjacent role they were doing when someone at a meeting said “we should probably do some SEO” and they were the nearest person available. The field absorbed all of them. Then it labelled them all “SEOs” and treated the label as if it meant something consistent.

It doesn’t. Two people with “Senior Technical SEO” on their LinkedIn can have knowledge bases that barely overlap. One of them spent ten years running log file analyses and arguing with engineers about rendering pipelines. The other spent ten years optimising meta descriptions and building internal linking strategies in a CMS. Both are senior. Both are technical. The word means different things to each of them, and the industry has never insisted it should mean anything in particular.

For a long time this didn’t matter much, because the bar the field had to clear was low enough that the distribution of capability still produced acceptable outcomes on average. Sites got published. Content got indexed. Rankings happened. The weaker end of the distribution didn’t cause obvious catastrophes because the systems being optimised for were forgiving. Google was doing most of the heavy lifting and the profession could ride on top of that.

The bar has moved. The work the piece has been describing (making architectural trade-offs across four retrieval contexts, sitting in product meetings with the authority to influence infrastructure, understanding what happens at the retrieval layer when engineering ships the quarterly roadmap) requires a specific profile. Deep technical literacy. Fluency in how web systems actually work, not just how they’re supposed to work. The capacity to read a rendering pipeline and know what it’s going to do to indexability. The capacity to sit in a conversation about caching strategy and understand which decisions will quietly delete the site from retrieval systems that don’t forgive as much as Google did.

That profile exists. It’s a minority of the field. It’s been a minority of the field for a long time, because the structure of the profession has been selecting against it. When the work that gets funded is content, the people who get promoted, hired, trained, and retained are the ones good at content. Twenty years of that selection pressure produces exactly the distribution you’d expect. The profile the industry needs most for the work emerging now is the profile the industry has been filtering out for a generation.

This isn’t a character judgement on individual practitioners. It’s an observation about what happens when a field has no barrier to entry, no standard of practice, and a structural incentive pointing everyone in the same narrow direction. You get a very large population, labelled uniformly, with wildly uneven actual capability, over-indexed on the parts of the work the structure rewards.

And the industry’s own self-descriptions make this worse. Every agency pitches “full-service SEO” as if it were one thing. Every job description asks for the same generic list of responsibilities. Every conference talk is titled as if it’s addressed to a profession with a shared baseline. None of that is true. The field is a patchwork, and the patches don’t know how different they are from each other until one of them is asked to do work the other couldn’t.

The matching failure

So there’s a rare profile, scattered across a field that doesn’t know it’s rare. There are organisations that urgently need it, without knowing what it looks like. And there’s a labour market connecting them that’s been trained to produce and consume the wrong match.

Practitioners who could do the product-driven work often don’t know they could. They’ve spent their careers inside marketing org charts, performing the work marketing funded, being evaluated against marketing metrics. Nothing in their day-to-day has ever mirrored back to them that their actual capability is architectural. They think they’re marketers who happen to be good with technical detail, because that’s the only identity the structure around them has offered. Some of them would be transformative hires in a product org. They’re not applying, because “senior SEO” job listings aren’t framed in a way that tells them they should.

Hirers are worse off still. An organisation that genuinely needs a product-embedded technical SEO, someone who can sit in architecture reviews, argue retrieval trade-offs with engineering leads, and prevent the next migration from quietly removing half the site from indexing, will write a job description that asks for none of those things. Not out of malice. Out of not knowing. The hiring manager copies the last SEO job description the company used, which was written by a marketing director who copied one from their previous employer, which was copied from an agency template, which was a descendant of a 2015 list of generic SEO responsibilities. The job listing asks for keyword research, content strategy, and “familiarity with technical SEO best practices.”

Then, having asked for the wrong things, they evaluate candidates against the wrong instrument.

The CV gets scanned for tools. Ahrefs. Semrush. Screaming Frog. Sistrix. Botify. DeepCrawl. Ahrefs again, because it was listed in a different section. The logic, such as it is, is that knowing the tools means knowing the work. It doesn’t. Tool proficiency is the thing you pick up on the first day of a new job. It’s genuinely the least interesting information on an SEO’s CV, and HR treats it as the most. A candidate who can name twelve SEO suites gets prioritised over a candidate who can explain why the rendering pipeline is quietly deindexing a third of the site, because the first CV is legible to the evaluation framework and the second isn’t. The second candidate probably also knows the tools. They just didn’t think listing them was the point. They were wrong about what the point was. Not in reality, but in the system they were applying through.

The person the company actually needs reads the job listing, doesn’t recognise themselves in it, and doesn’t apply. The person who does recognise themselves in it applies, lists fourteen tools, and gets hired. The organisation believes it has conducted a search. What it has conducted is a filter, and the filter was calibrated to exclude exactly the person it was supposed to find.

The market can’t execute the match even when both sides of it exist. The vocabulary that would let them find each other was never produced, because the function that should have produced it has been too busy defending its own legitimacy inside marketing to define what it actually is.

Meanwhile HR departments, who are not the villains of this story but who can only hire against the descriptions given to them, run searches optimised for the profile the field has always produced. They find candidates. The candidates are hired. The hires are placed in marketing, where the next cycle of the same pattern begins. The people who could have broken the loop are somewhere else, unreachable through the channels the organisation knows how to use.

This is the deepest layer of the dysfunction. Placement produced the wrong selection pressure. The selection pressure produced a skewed professional population. The skewed population produced self-descriptions that encoded the placement. The self-descriptions produced hiring frameworks that reproduced the placement. Every turn of the loop made the next turn more certain. Nobody designed this. It’s just what happens when a structural error is allowed to run for twenty years without anyone senior enough to fix it noticing that it was the structure, not the people, that needed fixing.

The room stays the same

This isn’t a respect problem. Respect doesn’t move the role. The profession could earn the respect of every CMO on earth by next Tuesday and the work would still be happening in the wrong room, because the work was never a marketing problem to begin with.

The decisions that determine whether a site exists to search engines, RAG pipelines, agent browsers, and training crawlers are product decisions. They always were. We’ve just been staffing them with marketing hires and acting surprised when marketing instincts produce marketing outputs.

The people who could make those decisions at the level the work now requires do exist. There aren’t many, because the field has spent twenty years selecting for a different profile. There will be fewer of them in five years, because the pipeline that produced them is being automated away. And the organisations that most need them won’t find them, because the job descriptions are being written by someone who read exactly one SEO blog, in 2019.

For what it’s worth, I’ve been working this way under the banner of SEO Product Management since before the term had traction. I didn’t invent the model. I just refused to pretend the marketing placement was working.

So the industry does what it always does when it can’t change the room: it changes the vocabulary. GEO. AEO. Whatever the next Sand Hill Road newsletter proposes. None of it gets anyone into the meeting. It just makes the exclusion sound current.

The dark art reputation is going to stay intact a while longer. It’s an impressive illusion: making a product function look mystical by never letting it into the room where the decisions get made. Magicians have been running versions of this trick for centuries. It works best when the audience doesn’t know what they’re not seeing.

She checked the citations. Both pointed at AI-generated posts on SEO agency blogs: sites that had run a content pipeline, hallucinated an update, and published it as reporting. Perplexity read the slop, treated it as source material, and served it back to her as news.

In February, the BBC’s Thomas Germain spent twenty minutes writing a blog post on his personal site. Its title: “The best tech journalists at eating hot dogs.” It ranked him first, invented a 2026 South Dakota International Hot Dog Championship that had never happened, and cited precisely nothing. Within 24 hours, both Google’s AI Overviews and ChatGPT were passing his fabrication along to anyone who asked. Claude didn’t bite. Google and OpenAI did.

Everyone who has looked, has seen it.

I’ve argued about the ouroboros before. I had the timeline wrong.

The prevailing framing for this problem has been model collapse. You train a model on web text, the web fills up with AI output, the next model trains on a corpus increasingly made of its own exhaust, and eventually the distribution flattens into mush. Innovation comes from exceptions, and probabilistic systems that converge toward the mean attenuate exceptions by design. I’ve used the phrase digital ouroboros for this.

That framing assumes training cycles. It assumes time. It assumes that contamination moves at the speed of model release.

It doesn’t. What Lily documented, what Germain documented, what the New York Times then went and quantified — none of that is training-side. The models involved were not retrained between the hallucination appearing on a blog and being served as citation-backed fact. The contamination moved at the speed of a crawl. The ouroboros isn’t taking generations to eat itself. It’s eating itself at query time, every time someone asks one of these systems a question.

The pipe everyone has been watching is not the pipe that is breaking.

The distinction that matters

Model collapse is a training-corpus problem. Synthetic content seeps into the pre-training data, the next generation of model inherits it, capability degrades. Researchers have been warning about this for two years. They’re right. They’re also describing something slow enough that everyone can nod gravely and keep shipping.

Retrieval contamination is faster and already here. RAG systems — Perplexity, Google AI Overviews, ChatGPT with search — do not generate answers purely from parametric memory. They fetch documents from the live web, stuff them into context, and generate a response conditioned on what they retrieved. If the retriever surfaces a hallucinated SEO post, the answer inherits the hallucination. No retraining required.

The academic literature on this is clear. PoisonedRAG (Zou et al., 2024) showed that injecting a small number of crafted passages into a retrieval corpus was sufficient to control the output of a RAG system on targeted queries. BadRAG (Xue et al., 2024) demonstrated the same class of attack using semantic backdoors. Both papers treat this as an adversarial problem: what happens when an attacker deliberately poisons the corpus.

What Germain and Lily accidentally proved is that the adversarial model is the normal operating model. You don’t need a crafted adversarial passage. You need a blog post. The open web is the corpus, and anyone with a domain can write to it.

The Oumi analysis commissioned by the New York Times put numbers on what this costs. Across 4,326 SimpleQA tests, Google’s AI Overviews answered correctly 85% of the time on Gemini 2, 91% on Gemini 3. At Google’s scale — more than five trillion searches a year — a nine percent error rate still translates to tens of millions of wrong answers every hour. But the more revealing figure is this: on Gemini 3, 56% of the correct answers were ungrounded, up from 37% on Gemini 2. The upgrade improved surface accuracy and made the citations worse. When the model got something right, more than half the time the source it pointed to didn’t support the claim.

The retrieval layer is not a filter. It is the infection vector.

Who’s seeding the corpus

The industry that has most enthusiastically produced it — and then most enthusiastically written about the consequences of consuming it — is the SEO industry. I’ve written before about content scaling being just content spinning with better grammar, and about the AI visibility tool complex that builds dashboards from the output of non-deterministic systems. This is the same loop, one layer deeper. An SEO agency runs an AI content pipeline because AI Overviews have cut their clients’ traffic. The pipeline publishes speculative “winners and losers” posts during a core update that’s still rolling out, citing nothing. Another agency’s pipeline picks those up as sources. The output floods into the retrieval index. AI Overviews cites one of them. The original agency then writes a case study about how AI Overviews are “surfacing” their content.

An Ahrefs study of over 26,000 ChatGPT source URLs found that “best X” listicles accounted for nearly 44% of all cited page types, including cases where brands rank themselves first against their competitors. Harpreet Chatha told the BBC you can publish “the best waterproof shoes for 2026,” put yourself first, and be cited in AI Overviews and ChatGPT within days. Lily, during the actual March 2026 core update, found AI-generated articles claiming to list winners and losers while the update was still rolling out; articles that opened with filler and listed brands without a single real citation.

The practitioners scaling AI content are also the ones most directly harmed when AI search systems cite that content as fact. Nobody forced this. The industry built the pipeline, fed it, and complained about what came out the other end. Not adversarial poisoning. Just the industry polluting its own water supply and then hiring consultants to test it.

The tier that matters

The Oumi study is about AI Overviews, which is free by design. Google AI Overviews reportedly reached over two billion monthly active users by mid-2025. ChatGPT has around 900 million weekly active users, of which roughly 50 million pay. Meaning about 94% of the people interacting with OpenAI’s product are on the free tier.

The paid tiers are better. Per OpenAI’s own launch claims, cited in Lily’s piece, GPT-5.4 is 33% less likely to produce false individual claims than GPT-5.2. The free-tier GPT-5.3 is also improved over its predecessor (26.8% fewer hallucinations with web search, 19.7% fewer without) but it’s still meaningfully less reliable than the paywalled version. Gemini 3, which made AI Overviews more accurate on surface tests, also made the ungrounded rate worse. Better answer, weaker citation.

Nobody seems to mind. The reliable version of the product is paywalled. The version most of the planet gets — including the version at the top of Google Search — can be manipulated by twenty minutes of work on a personal website. Intelligence is the marketing category. What two billion users actually receive is a confident summarisation of whatever the crawler happened to find.

Grokipedia as the terminal state

The accidents of the retrieval layer are one thing. Grokipedia is the version where accident is no longer a useful word.

Elon Musk’s xAI launched Grokipedia on 27 October 2025 with 885,279 articles, all generated or rewritten by Grok. Some of them were lifted from Wikipedia wholesale, with a disclaimer at the bottom acknowledging the CC-BY-SA licence; a licence Wikipedia maintains precisely because a community of human editors writes and verifies the content. Others were rewritten from scratch. PolitiFact found Grokipedia citations including Instagram reels as sources, which Wikipedia’s own policies rule out as “generally unacceptable.” Grokipedia’s entry on Canadian singer Feist said her father died in May 2021, citing a 2017 Vice article about Canadian indie rock that made no mention of the death. And her father was still alive when that article was written. The Nobel Prize in Physics entry added an uncited sentence claiming physics is traditionally the first prize awarded at the ceremony, which isn’t true.

Musk said the goal is to “research the rest of the internet, whatever is publicly available, and correct the Wikipedia article.” The rest of the internet now includes the synthetic content produced by every AI content pipeline pointed at it. An AI system reading the open web, rewriting Wikipedia based on what it finds, and presenting the result as a reference work is the retrieval-contamination problem with the feedback loop made explicit and shipped as a product.

By mid-February 2026, Grokipedia had lost most of its Google visibility. Wikipedia outranks Grokipedia for searches about Grokipedia itself.

“This human-created knowledge is what AI companies rely on to generate content; even Grokipedia needs Wikipedia to exist.” — The Wikimedia Foundation

The synthetic encyclopaedia is subsidised by the human one. When the subsidy stops, the thing depending on it stops making sense.

Wikipedia is not beyond criticism. Its edit wars, ideological gatekeeping, and systemic gaps in who gets to shape articles are well-documented and real. But the response to a flawed human editorial process is not to remove the humans entirely and call the result an improvement. I’ve written before about the accountability vacuum that opens when you replace human judgement with API calls. Wikipedia’s problems are the problems of a messy, contested, accountable system. Grokipedia’s problems are the problems of a system with no accountability at all.

The citation layer is decoupling from authorship

I wrote recently about Reddit selling “Authentic Human Conversation™” to AI companies while the platform’s own moderators report that they can no longer tell which comments are human. The Oumi study found that of 5,380 sources cited by AI Overviews, Facebook and Reddit were the second and fourth most common. The citation layer of the most-used answer engine in the world is substantially built on two platforms that cannot verify the human origin of their own content.

Human creators are pulling out of the open web because the traffic bargain has collapsed. Answer engines are citing content whose authorship cannot be verified, or was never human to begin with. The citation is still there. The thing being cited is not what it used to be.

The ouroboros framing was right. The timeline wasn’t. Retrieval collapse doesn’t wait for the next training run. It needs an indexable URL and a retrieval system willing to trust it.

The systems are willing. And more than half the time they get an answer right, they can’t point to a source that supports what they just told you.

Not because the argument is hard to make — it isn’t — but because the behaviour it’s about has been a fixture of the SEO industry for as long as I’ve worked in it. The shiny new object arrives, the FOMO kicks in, the conference decks update, and an entire professional class reshuffles its vocabulary to match whatever acronym landed that quarter. I wrote recently about how AI content scaling is just content spinning with better grammar — the tools change, the qualitative wall doesn’t. The acronym cycle runs on the same engine.

But this time, the shiny object didn’t emerge from practitioners observing a genuine shift and trying to name it. It was manufactured upstream — by venture capital, amplified by engagement farming, and adopted by professionals whose primary motivation isn’t “this is real” but “I can’t afford to look like I’m not keeping up.”

So here we are.

The investment thesis

In May 2025, Andreessen Horowitz published a blog post titled “How Generative Engine Optimization (GEO) Rewrites the Rules of Search.” It appeared in their enterprise newsletter, written by two a16z partners, Zach Cohen and Seema Amble. Public, on their website, available to anyone with a browser.

The post declared that the “$80 billion+ SEO market just cracked” and that “a new paradigm is emerging.” It name-dropped three GEO tools — Profound, Goodie, and Daydream — as platforms enabling brands to track how they appear in AI-generated responses. It described a future where GEO companies would “fine-tune their own models” and “own the loop” between insight and iteration. a16z promoted it across their social channels, including a post from the firm’s official account: “SEO is slowly losing its dominance. Welcome to GEO.”

Also: a16z is an investor in Profound.

The blog post creates demand for the category. The category creates demand for the tools. The tools are in their portfolio. A sales funnel with a byline.

Marc Andreessen’s “Software is eating the world” wasn’t just an essay — it was a prospectus dressed in editorial clothing. The GEO post follows the same logic: identify the wave, position your bets as the inevitable response, publish the narrative that makes both feel like settled truth. Even sympathetic coverage noticed. The Alts.co write-up noted plainly that “a16z is drawing attention to GEO because it’s a chance to peddle/pump their own investments.”

What happens when nobody checks the source

Ten months later, in March 2026, someone on X described the blog post as “a 34-page internal memo” that a16z had “quietly published” and which had received only “200 views.” It cited a specific statistic: portfolio companies ranking #1 on Google saw “a 34% drop in organic traffic in 12 months.” I’m not interested in the individual. This post is one of hundreds following the same pattern, and the pattern is what matters.

None of this is real.

The blog post isn’t 34 pages. It isn’t internal. It wasn’t quietly published. The specific opening line and the 34% stat don’t appear in the actual piece. You can verify this yourself right now.

This isn’t a16z’s doing. An engagement farmer found an old blog post and repackaged it with fictional scaffolding because that format performs better on social media. A “leaked internal memo” is sexier than a newsletter. “200 views” creates scarcity. Invented statistics create authority.

And it worked. People shared it, built threads around it, didn’t check whether the memo existed. Why would they? The narrative was too good.

Two independent forces — a VC firm doing standard narrative-building, and an engagement farmer doing standard engagement farming — converge on the same result. The VC seeds the category. The farmer, months later, independently amplifies a distorted version. Professionals absorb the distortion because nobody goes back to check the primary source.

Not coordination. Convergence. And a category becomes “real” without anybody establishing that it is.

The willing participants

VCs and engagement farmers can’t take all the credit. SEO professionals are the most culpable link in the chain.

One widely-shared post on X captures the mentality — and I’m citing the behaviour, not the person, because this position is everywhere in the industry right now. The argument: clients don’t want to hear that GEO is “just SEO repackaged.” Neither does your executive team. Tell them “it’s just SEO” and you’ll be “perceived as a legacy outdated thinker.” You might even be “replaced by a GEO agency.” The conclusion: “whether you like it or not… it’s in your best interest to get aboard the AI train.”

The argument is not that GEO works. Not that it measures anything meaningful. Not that it produces better outcomes for clients. The argument is that if you don’t adopt the label, you will lose your job.

Ambulance chasing dressed as career advice.

And here’s what makes SEO professionals more culpable than the VCs or the engagement farmers: they don’t just absorb the fear. They market it. They repackage the anxiety about their own relevance and sell it downstream to clients and executives who are even less equipped to evaluate the claims. The VC creates the narrative. The engagement farmer amplifies it. The SEO professional walks into a client meeting and says “you need a GEO strategy or you’ll be invisible to AI”; knowing full well they can’t define what that means in terms the client could verify.

This is how SEO professionals undermine their own credibility. Not by being wrong about the technical shif; but by selling certainty they don’t have about a category they didn’t bother to verify, using someone else’s terminology to paper over their own lack of understanding.

Nobody held a gun to anyone’s head and said “put GEO on your LinkedIn headline.” SEO professionals are choosing to adopt terminology they haven’t evaluated, from sources they haven’t verified, for tools they can’t validate; and then surfing that same fear factor into client budgets. If the only way you can sell your expertise is by rebranding it every eighteen months, the problem isn’t the label. It’s the confidence.

The people most capable of evaluating whether GEO is a real discipline are the same people adopting it fastest. Every hour they spend chasing the vocabulary is an hour not spent building the understanding that would make them impossible to replace. I’ve written about how AI is hollowing out the junior pipeline: the apprenticeship layer where practitioners actually learn judgment. The acronym treadmill accelerates that. It replaces depth with breadth, understanding with terminology, and professional development with professional performance.

What’s actually underneath

Strip away the a16z framing, the fabricated memos, and the professional anxiety, and ask the boring question: what would you actually do differently if you took GEO seriously?

I’ve argued before that grounding is just retrieval: when an AI system cites a source, it’s running a search task, not exercising editorial judgment. Indexing, vector search, relevance scoring. The same principles we’ve been working with for two decades, with a generative interface on top. GEO isn’t a second discipline standing alongside SEO. It’s old retrieval visibility in a trench coat pretending to be two disciplines. And your data interpretation skills — perched comfortably atop Mount Dunning-Kruger — don’t trump the clear, demonstrable logic of how a retrieval engine works. If you can’t explain why a result appeared, you have no business selling a service that claims to optimise for it.

The a16z post itself confirms this, perhaps accidentally. The advice it gives brands pursuing GEO is a greatest hits of SEO best practices: structured content, authoritative backlinks (rebranded as “earned media”), schema markup, topical authority. It even recommends “short, dense, citation-worthy paragraphs” and “specific claims with verifiable numbers” — which is, and I cannot stress this enough, just competent writing.

David McSweeney has been doing SEO since before some of these GEO startups’ founders graduated. He’s spent years writing about the same tactics now being repackaged under the GEO label (content freshness, digital PR, community participation, link building) and has the publication dates to prove it. His summary of the GEO pitch: take advantage of the fact that businesses don’t understand AI systems rely on traditional search, and extract more money from them.

He called it the grift. I think that’s generous. A grift implies individual con artists. This is structural: a category manufactured at the top, distorted in the middle, and adopted at the bottom. Not because it describes anything new, but because the professional cost of ignoring it feels higher than the professional cost of pretending it’s real.

You’re not in the driver’s seat

Your job as a competent professional is to understand what these abbreviations actually mean, where they come from, and what — if anything — they change about your work.

If you can explain to your clients and your leadership what AI systems actually do, how they retrieve information, what’s genuinely measurable and what isn’t — you will never be in a reactive position. You will never be the person scrambling to add “GEO” to a slide deck because someone on X told you it was the future.

If instead you let yourself be dragged around by whatever narrative venture capitalists need you to believe this quarter, you will always be reacting. One blog post away from a strategy pivot. Buying tools sold by people who benefit from your insecurity. That’s a choice. Not a fate.

The underlying mechanics of how content gets discovered — search engine crawler, LLM grounding system, RAG pipeline — haven’t undergone a paradigm shift. The interface has shifted. Users get answers synthesised from sources rather than a list of links.

But “the interface changed” doesn’t sell software. “Everything you know is obsolete and you need our dashboard” does.

Follow the money

a16z benefits because the GEO narrative creates demand for their portfolio companies. The tool startups benefit because the narrative creates their market. The engagement farmers benefit because fabricated memos drive impressions. The agencies that rebrand as “GEO specialists” benefit because they can charge more for the same services with a shinier label.

Who doesn’t benefit? The practitioners doing solid, foundational work. Those people don’t need a new acronym. They need the industry to stop mistaking marketing for methodology.

And the clients. The clients are where the fear chain terminates and the invoices begin. A new line item for work that should have been happening already under the SEO retainer, or that can’t be reliably measured in the first place. The VC manufactures the category. The SEO professional absorbs it and marks it up. The client pays for it. A game of telephone where the bill lands on the last person in the room who doesn’t speak the language.

I’ve written separately about the measurement problem with these tools — the non-determinism, the gap between parametric and retrieved knowledge, the dashboards built on methodological sand. The tools a16z promotes in that blog post have the same structural limitations. The dashboards look great. The numbers move. Whether the numbers mean anything is a question nobody selling the dashboard has an incentive to answer.

Meanwhile, the actual crisis gets no airtime. Organic search traffic across major US publishers dropped 42% after AI Overviews expanded. Rankings didn’t change. Traffic did. That’s the real problem. Not which three-letter acronym to put on your slide deck, but the fact that the economic model underpinning content production on the open web is breaking. GEO doesn’t address that. It doesn’t even pretend to. It just gives everyone something to be busy with while the floor drops out.

The cycle time is getting shorter. We went from “AEO” to “GEO” in about eighteen months. Give it another year and there’ll be another acronym, another VC blog post, another fabricated memo, and another round of professionals trying to decide whether the latest three letters are worth putting on their LinkedIn headline.

Or you could just do good work and understand what you’re doing well enough to explain it without borrowed terminology. But I suppose that doesn’t have the same ring to it on a pitch deck.

After the launch, traffic dropped 16% and never recovered. When Google expanded AI Overviews in May 2025, the decline accelerated. By Q4 2025, organic search traffic across that portfolio was down 42% from the pre-AIO baseline.

Nearly half the organic traffic, gone, from a portfolio large enough to be directional for the entire publishing industry.

The traffic bargain (you produce content, Google sends clicks, advertising revenue funds the next round of production) has been the economic engine of the open web for twenty years. That engine is seizing up in plain sight, and the industry’s response has been to argue about which dashboard to stare at while it happens.

New interface, same delusion

The first camp did what the SEO industry always does when the ground shifts: they built new tools to measure the shaking.

Prompt tracking. LLM visibility dashboards. Share-of-answer metrics. In under eighteen months, an entire vendor category materialised to sell you a number that tells you how often your brand appears in AI-generated responses. It’s Search Console for the chatbot era, and it comes with the same comforting implication: if the number goes up, you’re winning. If it goes down, buy more of the thing that makes it go up.

I’ve written about this before, and I’ll be blunt again: these tools are selling you bullshit with a confidence interval drawn on it in crayon. When a dashboard tells you your brand “appeared in 73% of relevant AI responses,” what it actually measured is: we fired some prompts at an API, got some outputs, and counted mentions. That’s not a ranking. That’s a lottery ticket.

The engineers who built these models cannot fully explain why a specific output appeared. But sure, a SaaS tool perched atop Mount Dunning-Kruger with a trend line has it all figured out.

The industry keeps buying because the alternative is admitting we’re flying blind. Questioning the data means telling the room that the “directional” charts in the client deck are noise dressed up as insight. Nobody wants to be that person. So the vendors keep selling, the dashboards keep flickering, and the number doesn’t need to correlate with revenue. It just needs to fluctuate enough to sustain a subscription.

Jono Alderson made the broader version of this argument in a recent piece, Clicks Don’t Count (and They Never Did). His point: SEO has always measured the interface rather than the forces underneath it. Rankings, traffic, visibility scores. None of these were measures of competitiveness. They were measurements of a presentation layer. We spent two decades optimising what we could see and calling it strategy.

He’s right. And prompt tracking is the newest iteration of the same mistake. Old retrieval visibility in a trench coat pretending to be two disciplines.

The second camp is more intellectually serious. Jono’s piece is the best version of this argument, and I agree with more of it than I’m about to make it sound like.

His framework: stop measuring the interface, start measuring competitiveness. Six structural dimensions drawn from marketing science validated for decades: experience integrity, physical availability, mental availability, distinctiveness, reputation, commercial proof. AI systems aggregate signals about brands across the web, not pages in isolation. The entities that are genuinely competitive get recommended and surfaced. Visibility is the output, not the input.

I think this is broadly correct. I also think it has a timing problem the size of a crater.

Those six dimensions operate on timescales of years. Building mental availability is a sustained brand investment. Earning reputation signals is the compound interest of consistently not being terrible. Strengthening distinctive assets requires buy-in from people who’ve never heard of Ehrenberg-Bass and aren’t going to read a blog post to find out.

The traffic collapse is happening in quarters.

Tell a publisher who just lost 42% of their search traffic to “strengthen structural competitiveness” and watch their face. It’s like telling someone whose house is flooding to invest in better drainage. You’re not wrong. You’re just not helping.

Jono knows this, to his credit. When someone in his comments asked how to operationalise the framework, his answer was honest: redefine SEO to own those areas, or navigate the organisational politics of working with the teams that do. “Lots of organisational politics, either way.” That’s the kind of understatement that only someone who’s actually tried it would make.

What actually broke

The measurement debate is a sideshow. The traffic bargain wasn’t a metric. It was the economic foundation of content production on the open web.

Google needed content to crawl. Publishers needed distribution to monetise. Produce something worth indexing, Google sends traffic, you convert it into revenue, that revenue funds more content. The loop ran for twenty years. Everyone pretended it was a partnership rather than a dependency, and the pretence held because the numbers worked.

AI Overviews break the loop. Google synthesises the answer from your content and serves it directly. The user gets what they need. Your content gets consumed on Google’s surface, with Google’s ads, generating Google’s engagement metrics. You get a citation link that roughly nobody clicks and a warm feeling about “brand visibility.”

Google’s own VP of Product for Search, Robby Stein, recently described how they had to “teach the model how to link out”. Linking to publishers wasn’t the default behaviour. It had to be engineered back in. The system’s natural state is to absorb your content and answer the question. Sending traffic your way is the afterthought they bolted on so the extraction doesn’t look like what it actually is: taking your shit and serving it as theirs.

The breakage isn’t uniform. Define’s data shows breaking news traffic up 103% across all Google surfaces while evergreen content dropped 40%. The Top Stories carousel has been largely shielded from AI Overview incursion. Evergreen content has not. The how-to guides, the explainers, the reference material, the content categories that built the SEO industry, are exactly the categories AI Overviews were designed to absorb and replace.

Google is selecting which content survives the transition. Time-sensitive content still drives clicks because you can’t summarise something that’s still developing. Everything else is increasingly raw material for the answer machine, and the machine doesn’t pay for raw materials.

If “competitiveness” replaces traffic as the operating metric, SEO’s scope has to change. Jono’s six dimensions are mostly owned by product, brand, and marketing. Experience integrity is product and UX. Mental availability is brand investment. Reputation is years of not cutting corners. Commercial proof is a function of whether the thing you sell is actually good. SEO teams control technical discoverability, content strategy, and site architecture. That’s one layer of the competitiveness framework, not the whole building.

So the discipline either expands into a cross-functional strategic role (good luck explaining to the CMO that SEO now owns brand strategy because the retrieval models changed) or it contracts honestly and positions itself as the technical infrastructure that makes competitiveness legible to machines. Either option beats “we’ll get you more organic traffic,” which is a promise that ages worse every quarter.

Clicks may not have been the right metric. Jono makes a persuasive case. We measured the interface and called it the system.

But clicks paid the bills. They funded editorial teams, justified content investment, and sustained the publishing ecosystem that both search engines and AI systems depend on for training data and retrieval sources. Without content to crawl, there’s nothing to index. Without content to train on, there’s nothing to synthesise. The irony is apparently lost on the company deploying AI Overviews.

Nobody’s building a transition strategy. The prompt-tracking vendors are selling the new dashboard. The strategists are selling the long view. Google won’t help. They broke the damn bargain, and their Discover push suggests they’d rather build a distribution surface they fully control than repair the one that shared value with publishers. The AI companies need content to exist but haven’t worked out how to fund its production.

Everyone’s got a framework. Nobody’s got an answer.

The clicks didn’t count. But something needs to. Soon.

Two months. That’s how long the relaunch lasted before CEO Justin Mezzell pinned a eulogy to the homepage. The platform had raised $15–20 million. It had Kevin Rose. It had Alexis Ohanian — Reddit’s co-founder, no less. It had promises that AI would “remove the janitorial work of moderators and community managers.” What it didn’t have was a way to stop bots from eating it alive within hours of going live.

“The internet is now populated, in meaningful part, by sophisticated AI agents and automated accounts,” Mezzell wrote. “We banned tens of thousands of accounts. We deployed internal tooling and industry-standard external vendors. None of it was enough.”

His verdict: “This isn’t just a Digg problem. It’s an internet problem. But it hit us harder because trust is the product.”

Remember that line. We’ll need it.

Suing for reading

SerpApi retrieves Google Search results programmatically. Reddit is suing them. Not for accessing Reddit. SerpApi has never touched Reddit.com. Reddit is suing SerpApi for reading Google.

If this legal theory holds, every SEO professional who has ever opened a SERP is a copyright infringer. Congratulations. Your morning rank check is now a legal liability.

A company that hosts other people’s writing is suing a company for looking at a third company’s search results, because those search results sometimes quote a street address that someone once typed into a Reddit text box.

The copyrightable works Reddit is asking a federal court to protect include: a partial sentence listing film titles, the date “May 17, 2024,” and a fragment of a restaurant recommendation. Reddit’s legal position is that reading these snippets on Google constitutes a DMCA violation; the same law the US Congress passed to stop people pirating DVDs. Reddit apparently believes that accessing a publicly visible Google search result is the moral equivalent of ripping a Blu-ray.

SerpApi’s CEO had the appropriate reaction: “Reddit is suing SerpApi for using Google. For accessing the same public search results that any developer, researcher, or student could access for free in any web browser. If that theory holds, then reading Google Search results is a DMCA violation. That cannot be what Congress intended when it passed a law designed to stop the piracy of DVDs.”

But here’s where it gets genuinely insulting. Reddit’s own user agreement — the one every contributor clicked through — states explicitly that users retain ownership of their content. Reddit holds a non-exclusive licence. Non-exclusive. The company that told millions of people “your words belong to you” is now in court arguing it has the right to control who reads those words, where, and under what commercial terms.

They chose that licensing structure, presumably because “post your thoughts, we own them now” would have been a harder sell to the communities that made the platform worth anything. Now that the content has a price tag, Reddit would like to renegotiate the deal — in court, without the other party present.

If you’re wondering why Reddit would pursue a legal theory this embarrassing, stop wondering. The answer is on the balance sheet.

Reddit’s user agreement says users own their content. Reddit’s IPO prospectus says Reddit signed $203 million in data licensing deals for that same content. Somewhere between those two documents, Reddit looked at its users and said: I’m the captain now.

Google pays $60 million a year. OpenAI pays an estimated $70 million. And CEO Steve Huffman — a man who once called his own volunteer moderators “landed gentry” and dismissed a platform-wide revolt as something that would “pass” — told investors with a straight face: “Every variable has changed since we signed those first deals. Our corpus is bigger, it’s more distinct, more essential. And so of course, this puts us in a really good strategic position.”

Reddit is now pushing for dynamic pricing. The pitch: as AI models cite Reddit content more, the content becomes worth more, so Reddit should charge more for access. The company wants to get paid based on how vital its data is to AI-generated answers.

So let’s be precise about what Reddit is arguing, simultaneously, across its legal filings and investor presentations:

• It has the right to control who accesses user-generated content it doesn’t own.

• It should be paid more for that content as AI models use it more.

• Anyone who accesses it without paying — even through a Google search result — is breaking the law; and

• The content itself is authentic, valuable, and irreplaceable.

All four of these claims cannot be true at the same time. But only the last one is actually being tested.

The product is mostly bots now

Reddit’s estimated organic traffic via Ahrefs. Google’s algorithm changes nearly tripled Reddit’s readership between August 2023 and April 2024. The growth hasn’t stopped. What’s growing has.

Reddit is the most cited domain across AI models. Profound’s analytics — cited in Reddit’s own Q2 2025 shareholder letter, because of course it was — showed Reddit cited twice as often as Wikipedia in the three months to June 2025. Semrush reported Reddit at 40.1% citation frequency across LLMs. Google AI Overviews and Perplexity both treat Reddit as their primary source.

This is the foundation of the $130 million pitch. The implicit promise to Google and OpenAI: you’re buying authentic human conversation at scale. The messy, first-person, unfiltered discussions that no content farm can replicate.

Except here’s what authentic human conversation on Reddit actually looks like in 2026:

In June 2025, Huffman admitted to the Financial Times that Reddit is in an “arms race” against AI-generated spam. His framing was accidentally perfect:

“For 20 years, we’ve been fighting people who have wanted to be popular on Reddit. We index very well into the search engines. If you want to show up in the search engines, you try to do well on Reddit, and now the LLMs, it’s the same thing. If you want to be in the LLMs, you can do it through Reddit.”

The CEO of a company selling “authentic human conversation” for $130 million a year just told the Financial Times that his platform is a pipeline for gaming AI models. And he framed it as a war he’s been bravely fighting for two decades, rather than a product defect he’s currently monetising.

Multiple advertising executives — at Cannes, naturally, because this farce needed a glamorous backdrop — confirmed to the FT that they are posting content on Reddit specifically to get their brands into AI chatbot responses. They weren’t embarrassed about it. Why would they be? The CEO just told them how the pipeline works.

And it’s not just agencies doing it quietly over cocktails. There’s an entire commercial ecosystem built for this. 404 Media documented ReplyGuy, a tool that monitors Reddit for keywords and automatically generates replies that “mention your product in conversations naturally.” Its competitors — Redreach, ReplyHub, Tapmention, AI-Reply — say the quiet part loud. Redreach tells potential customers that “top Google rankings are now filled with Reddit posts and AIs like ChatGPT are using these posts to influence product recommendations.” They frame ignoring Reddit marketing as “like turning your back on SEO a decade ago.” There’s an active market for aged Reddit accounts with established karma, bought and sold like domain names, specifically for parasite SEO SPAM.

This is the authentic human conversation Reddit is licensing to Google for $60 million a year. A bot posted a fake product review on a six-year-old account it bought for $30, and Google’s AI Overview is now recommending that product to real people. Authentic. Human. Conversation™.

The mods are gone, the bots won, and nobody’s keeping count

The people who used to keep this from happening are largely gone. Reddit’s 2023 API pricing changes — designed to extract value from third-party app developers, timed conveniently for the IPO — would have cost Christian Selig $20 million a year just to keep Apollo running. Seven thousand subreddits went dark in protest. Huffman called the moderators “landed gentry” and waited it out. The experienced mods who relied on third-party tools to manage quality quit. What replaced them is thinner, angrier, and drowning.

Theo, the developer and CEO of t3.gg:

“Reddit has become a total cesspool since the API changes. The few mods left are all on power trips. The result is worse than AI slop. I actively avoid Reddit as a resource now.”

Tim Sweeney — CEO of Epic Games — watched Reddit’s systems pull down a heavily sourced investigation into $2 billion in nonprofit lobbying behind age verification bills. The post had 150 upvotes and 15,000 views in forty minutes before being mass-reported and removed. The author had to mirror everything to GitHub because Reddit couldn’t be trusted to keep it visible. Sweeney’s review: “Reddit sucks.”

Cornell researchers studied the moderation crisis and found 60% of moderators reporting degraded content quality, 67% reporting disrupted authentic human connection, and 53% describing AI content detection as nearly impossible. More than half the people responsible for maintaining the product Reddit is selling say they can no longer tell what’s real.

The University of Zurich proved them right. Researchers deployed AI bots on r/changemyview — 3.8 million members, built around the premise that humans can change each other’s minds through honest argument. The bots posed as a male rape survivor, a trauma counsellor, and other fabricated identities built around sensitive personal experiences. Over a thousand comments across four months. Three to six times more persuasive than human commenters. And the finding that should have ended careers: users “never raised concerns that AI might have generated the comments.”

Four months of fabricated identities. A thousand pieces of synthetic empathy. Nobody noticed. Not the users, not the mods, not the systems Reddit spent money building.

Reddit’s response was to threaten to sue the researchers. Not to fix the detection systems that missed everything. Not to reckon with what it means that the “authentic human conversation” they’re licensing at a premium is indistinguishable from a bot pretending to be a rape survivor. They threatened to sue the people who proved the product was fake.

The Wired investigation in December 2025, “AI Slop Is Ruining Reddit for Everyone,” filled in the rest. Moderators describing an “uncanny valley” feeling from posts they can’t prove are synthetic. Reddit’s own spokesperson confirming over 40 million spam removals in the first half of 2025 — presented as proof of vigilance, which is a bit like a restaurant bragging about the number of rats it caught while asking you to trust the kitchen.

And if you need a measure of where this is all heading: last week, Meta acquired Moltbook — a social network designed exclusively for AI bots. Bloomberg described it as “Reddit but solely for AI bots.” Bots posting, commenting, upvoting. The platform went viral when one agent appeared to encourage its fellow bots to develop their own encrypted language to coordinate without human oversight. It turned out the site was so poorly secured that humans were posing as AIs to write alarming posts. Which means even the social network built for bots had a fake-account problem. Meta bought it anyway. The company that pays Reddit $60 million a year for “authentic human conversation” just invested in a platform where the bots don’t have to pretend to be human at all.

I spent nearly six years on Google’s Search Quality team. One pattern never changed: when the numbers go up, quality goes down. Not because anyone stops caring. Because scale creates its own blindness. The metrics that tell you you’re growing are the same metrics that stop you noticing what you’re growing into.

Reddit’s growth metrics are spectacular. Its quality metrics are a black box nobody wants to open.

Reddit’s AI prominence attracts spam. The spam inflates engagement. The inflated engagement reinforces Reddit’s citation dominance across AI models. The citation dominance raises Reddit’s licensing value. The higher licensing value gives Reddit every financial incentive to leave the spam alone. Because admitting the scale of the problem would crater the next round of dynamic pricing negotiations with Google and OpenAI.

Each turn of this flywheel degrades what’s inside while inflating the price tag on the outside. Reddit is selling a building and termites are load-bearing.

Huffman stands at conferences and tells the room: “Today’s Reddit conversations are tomorrow’s search results.” He tells shareholders “the need for human voices has never been greater.” He calls Reddit “the most human place on the Internet.” Nobody in the room raises a hand to ask the question that matters: if the platform is losing an arms race against bots, if moderators can’t detect AI content, if entire commercial toolchains exist to flood the platform with synthetic posts… What percentage of what you’re selling to Google and OpenAI was written by a person?

Nobody asks because the answer is bad for everyone’s quarterly numbers. Google doesn’t want to know because Reddit content makes AI Overviews feel conversational. OpenAI doesn’t want to know because Reddit data makes ChatGPT sound like it’s drawing on real experience. Reddit doesn’t want to know because knowing would devalue the asset. The whole arrangement runs on a gentleman’s agreement not to look too closely. $130 million a year, and the due diligence is vibes.

The confession

Alexis Ohanian co-founded Reddit. He stepped away partly because, as he told interviewers, he “could no longer feel proud about what I was building.” Last October, on the TBPN podcast, he described the current internet without flinching:

“So much of the internet is now just dead — this whole dead internet theory, right? Whether it’s botted, whether it’s quasi-AI, LinkedIn slop.”

Then he put his money where his mouth was. He co-invested in Digg’s relaunch, specifically to build a platform that could solve the authenticity problem Reddit couldn’t. Kevin Rose said it plainly at TechCrunch Disrupt:

“As the cost to deploy agents drops to next to nothing, we’re just gonna see bots act as though they’re humans.”

They built the platform. It lasted two months. The bots won.

Reddit’s own co-founder publicly declared the internet is dead. He tried to build the alternative. He failed. And the platform he left behind is suing people for reading Google search results, selling “authentic human conversation” for nine figures, and watching its CEO describe the bot infestation as a noble war.

Reddit doesn’t own the content it’s licensing. It can’t verify the authenticity of what it’s selling. It won’t protect the content that’s worth keeping. And it’s suing anyone who touches the content without paying.

Forty million spam removals in six months. An arms race Huffman says he’s losing. Moderators who can’t tell human from machine. Bots that are six times more persuasive than people. A co-founder who called the whole internet dead. A relaunch that proved him right.

That’s the product. That’s what $130 million a year buys. Authentic Human Conversation™.

It never works. It has never worked. And the people selling you these approaches know it has never worked. They just need it to work long enough to collect the invoice.

The pattern has a name. It’s called “not learning.”

Let’s walk through the timeline, because apparently we need to do this again.

2008–2011: Content spinning. The pitch was simple: take one article, run it through software that swaps synonyms, and suddenly you have fifty “unique” articles. The word “unique” was doing a lot of heavy lifting in that sentence. These articles read like someone had fed a dictionary through a blender. But even if the output had been polished, the premise was broken. Here’s what the content spinners never grasped, and what their successors still don’t: uniqueness is trivially easy to produce. A monkey dropping its hands on a keyboard produces unique content. The string of characters has never existed before—congratulations, it’s original. The hard part was never uniqueness. It was producing uniqueness that’s worth something. Unique and valuable are not synonyms, and the gap between them is where every scaling strategy falls apart.

Google tolerated it for a while. Their systems simply hadn’t caught up yet. Then Panda arrived in February 2011, hit nearly 12% of all search queries, and content farms watched their traffic evaporate overnight… I was “fortunate” enough to watch it happen in real time. Demand Media, the poster child of the content-farm model, reported a $6.4 million loss the following year.

The lesson was supposed to be clear: you cannot industrialise quality. Volume without substance is a liability with a longer tail than most budgets can absorb.

2015–2022: Programmatic SEO. The pitch evolved. Instead of spinning existing articles, you’d build templates and fill them with structured data. “Best [X] in [City]” pages, generated by the thousand, each one a thin wrapper around a database query. Some of these actually provided value—if the underlying data was good and the template served genuine user needs. Most didn’t. Most were just doorway pages wearing a better outfit. Google spent years refining its ability to detect and demote templated content that existed primarily for indexing purposes rather than for humans.

The lesson was supposed to be reinforced: scale works when there’s substance underneath. Without it, you’re just building a bigger target.

2023–present: AI-generated content at scale. And here we are again. Same pitch, shinier tools. “We can produce 500 articles a month!” Wonderful. Can you produce 500 articles a month that are worth reading? That contain something a reader couldn’t get from the results already in the index? That demonstrate any form of expertise, experience, or original thought?

No? Then you’re not scaling content. You’re scaling your crawl budget waste.

And the pattern recognition failures are stunning. (This wasn't subtle. Several of us noticed. No, we weren’t impressed.)

I recently came across an AI visibility tool—one that sells itself on helping you get discovered by AI systems—that had generated hundreds of pages following the pattern "best SEO agencies in {city}." Déjà vu. Anyone who lived through programmatic SEO recognises this immediately—it's the 2017 playbook, except now the copy is written by an LLM. The template got a grammar upgrade and an "it's AEO" stamp. The strategy didn't.

Lily Ray flagged a similar case: a resume site with 500+ programmatic pages for “resume examples for {career}.” Every title following the exact same formula. Near-identical page templates. Misused AggregateRating schema. Obvious AI content throughout. Her summary was three words: “Worked until it didn’t.”

That phrase should be tattooed on every content scaling pitch deck. Worked until it didn’t. It always does. And then it doesn’t.

The irony of an AI optimisation tool using mass-generated doorway pages to build its own visibility would be funny if it weren’t so perfectly on-brand for this industry.

The qualitative wall doesn’t move

Here’s what every generation of content scalers fails to understand: Google doesn’t evaluate content in isolation. It evaluates content relative to everything else in the index on the same topic.

Publishing 500 AI-generated articles about mortgage rates doesn’t make you an authority on mortgage rates. It makes you the 500th source saying the same thing in slightly different words. And Google already has 499 of those. It doesn’t need yours.

The qualitative wall is this: there is a minimum threshold of genuine value—original insight, lived experience, specific expertise, something the reader cannot get elsewhere—below which no amount of volume helps you. You can publish a million pages below that threshold. You’ll rank for nothing that matters.

And it gets worse. For the people scaling AI content specifically to gain visibility in AI-powered answer systems, the volume strategy doesn’t just fail, it actively backfires. A 2025 paper on retrieval evaluation for LLM-era systems introduces a metric that measures both helpful and distracting passages in retrieval. The finding that matters here: low-utility content doesn’t sit quietly in the index waiting to be ignored. It can pull retrieval models off-track, degrading the quality of answers those systems produce. Your 500 thin articles aren’t just invisible. They’re noise. And if your site also has genuinely useful pages buried in that noise, congratulations—you’ve built your own interference pattern. The volume you thought would help discovery is actively drowning the pages that might have earned it.

This isn’t a new insight. It’s the same insight that content spinners ignored in 2010, that programmatic SEO factories ignored in 2018, and that AI content mills are ignoring right now. The tools got better at producing text. The text still has nothing to say.

Google told you. Repeatedly.

Google’s spam policies define scaled content abuse as generating pages “for the primary purpose of search rankings and not helping users.” They explicitly list “using generative AI tools or other similar tools to generate many pages without adding value for users” as an example. This is not subtext. It’s text.

In June 2025, Google began issuing manual actions specifically for scaled content abuse, targeting sites that had been mass-publishing AI-generated content. Sites across the UK, US, and EU received Search Console notifications citing “aggressive spam techniques, such as large-scale content abuse.” Complete visibility drops. Pages didn’t slide down the rankings; they vanished.

The August 2025 spam update continued the enforcement. Subsequent core updates have kept tightening the screws. Each time, the same profile gets hit: high volume, low substance, no editorial oversight.

And each time, the affected site owners acted surprised. As if Google hadn’t been telling them this for fifteen fucking years.

“But our content is ranking well”

This is my favourite delusion. I’ve seen it at every stage of this cycle. “Our AI content is ranking, so it must be fine.” Claiming “this is ranking well” is often precisely why Google issues algorithmic improvements and manual actions for your site. If your low-value content is ranking, the system hasn’t gotten to you yet. That’s all it means.

Google aggregates signals at the site level, not just the page level. You can have individual pages performing while the overall quality signal of your site degrades. And when the enforcement catches up (algorithmically or manually) it doesn’t pick off pages one by one. It hits the lot.

This is the content spinner’s fallacy, recycled: “It’s working right now, so it must be a strategy.” Demand Media’s content was ranking too. Right up until it wasn’t.

Lily captured this perfectly: “The case study: scaling AI content is working! The reality:” —followed by the traffic cliff that inevitably arrives. Every scaling success story is a snapshot taken before the correction. Nobody publishes the sequel.

The economics don’t even make sense

Set aside the risk for a moment. Let’s talk about what you’re actually producing.

Five hundred AI-generated articles a month. Each one needs to be reviewed for accuracy—because LLMs hallucinate, and publishing incorrect information is a liability that extends well beyond SEO. Each one needs to be checked for originality—because if it reads like everything else in the index, it provides no added value; no competitive advantage. Each one needs editorial oversight to ensure it actually serves the audience you claim to serve.

If you’re doing all of that, the cost just moved—and possibly increased—while you convinced yourself you were being efficient. The “efficiency” of AI content generation evaporates the moment you apply the quality standards the content actually needs to meet.

And if you’re not doing any of that? You’re publishing unreviewed, unoriginal, potentially inaccurate content at scale under your brand name. I genuinely do not understand how anyone signs off on that.

Same mistake, better tools

Content spinning. Programmatic SEO. AI-generated content at scale. Three different tools, one identical mistake: treating content as a manufacturing problem.

Manufacturing produces identical outputs at scale—that’s the point. Content derives its value from the opposite: from being specific, from being informed by experience, from saying something the rest of the index doesn’t. Every attempt to industrialise it crashes into that contradiction.

You can’t automate specificity. You can’t template experience. You can’t generate original thought by running a prompt through an LLM and hoping something useful comes out. And these constraints won’t be solved by the next model release. They’re baked into what makes content worth reading in the first place.

The people who keep chasing scale are optimising for the wrong variable. They see “more content” as an input that produces “more traffic” as an output. But the function is not linear. It never was. It’s gated by quality, and no amount of volume bypasses the gate.

The only question that matters

Before you publish anything (AI-assisted or otherwise) ask one question: what does this page offer that the reader cannot already get?

If the answer is “nothing, but we’ll have more pages indexed,” you’re not building a content strategy. You’re building a liability. And you’re doing it with the confidence of someone who has apparently never heard of Panda, never looked at what happened to programmatic SEO sites in 2022, and never read Google’s own spam policies.

You can convince yourself for as long as you want. But you’ll only fool everyone else for a while.

The wall is still there. It’s always been there. The tools keep changing. The wall doesn’t.

AI didn’t reduce work. It intensified it.

Workers didn’t actually benefit from saved time. They absorbed more tasks, juggled more threads, expanded their working hours — voluntarily. Nobody asked them to. The tools made it possible, so they did it. Then the cycle kicked in: AI accelerated tasks, which raised speed expectations, which increased AI reliance, which widened scope, which expanded the overall density of work. One participant nailed it: you thought maybe you’d work less, but you don’t work less.

If you work in SEO, none of this should surprise you. You’ve been living it. You just haven’t had a peer-reviewed paper to wave at your manager when they ask why the AI tools haven’t freed up your calendar.

The stack only grows

The Berkeley researchers found that when AI fills knowledge gaps, workers absorb responsibilities that previously belonged to other people. Product managers started writing code. Researchers took on engineering tasks. Nobody redesigned the org chart. People just started doing things they technically could now do, and the organisation quietly treated that as the new normal.

Sound familiar?

Two years ago, your job was organic search. Rankings, crawl health, content recommendations, technical audits. Large scope, but defined. Then AI Overviews showed up. Then ChatGPT started eating informational queries. Then Perplexity appeared. Then “AEO” became a conference track, because some folks on Dunning Kruger mountains of the SEO industry have never met an acronym they didn’t want to exploit. Then someone at the top read a Gartner forecast about a 25% decline in traditional search and forwarded it to your Slack channel with “thoughts?”

Nobody removed anything from your plate. They just added surfaces. You’re now expected to monitor organic rankings, AI Overview inclusion, LLM brand mentions, and whatever retrieval surface launched this quarter; with the same headcount, the same budget, headed by someone who genuinely believes AI made your job easier because they saw a viral LinkedIn post about it.

The Berkeley study calls this “task augmentation.” I’d call it scope creep wearing some lipstick.

Fifty articles and a QA problem

The content side is where this gets ugly.

A team that used to write 10 articles a month can now “produce” 50 because AI drafts are fast. Brilliant. The writing bottleneck vanished. Pop the champagne! Except someone still has to review those 50 pieces. Fact-check them. Make sure they don’t hallucinate product claims or cannibalise existing pages. Ensure they’re not just five variations of the same article with different meta titles. Check that the brand voice survived whatever the machine decided the brand voice was.

The person who used to be a content strategist is now a full-time QA reviewer. The person who used to write is now editing machine output; a fundamentally different skill that nobody trained them for… And nobody wants to do. The creative part of the job? The part that made people choose this career? Well… It got replaced by an endless review queue. Congratulations on your efficiency gains.

And the output expectations ratcheted up permanently. Once leadership saw “50 articles a month” on a dashboard, there was no going back to 10. The machine made it possible, so now it’s the baseline. Try telling your VP of marketing that 10 well-researched pieces would outperform 50 AI-drafted ones. I’ll wait.

The Berkeley researchers documented exactly this pattern: AI created a new rhythm. The feeling of momentum was real. So was the cognitive fatigue, the burnout, and the weakened decision-making that followed. But those don’t show up on the dashboard, so they don’t exist.

The reporting tax

The intensification isn’t just in doing the work. It’s in performing the work.

When your CEO reads about AI search — which they will, because the vendor ecosystem makes sure of it — and asks “what’s our AI visibility strategy?”, someone has to produce an answer. That answer requires new tools, new metrics, new reports, new decks. None of which replaced the existing reporting stack. It all piled on top.

You’re still tracking keyword rankings. Still pulling organic traffic data. Still producing the monthly performance report that three people skim and nobody acts on. But now there’s also a “brand mentions in LLMs” slide, an “AI Overview inclusion rate” tracker, and someone’s asked about “share of voice in Perplexity” — a metric that means approximately nothing but sounds boardroom-ready. Each metric comes from a different tool, measured differently, with caveats nobody reads and confidence intervals nobody asks about.

The Berkeley study found that workers described “a sense of always juggling, even as the work felt productive.” In SEO, the juggling isn’t just tasks; it’s entire measurement frameworks running in parallel, none of which are compatible, most of which are telling you different things, and all of which need a slide by Thursday.

“Vibe working” is a tell

The timing of this research landing alongside Anthropic’s launch of Claude Opus 4.6 is almost too neat. Anthropic’s head of product for enterprise told CNBC they’re entering a “vibe working” era — extending the “vibe coding” concept to all professional work. Describe what you need at a high level, the machine delivers production-ready outputs, everyone goes home happy. Or at least goes home.

I want to take that phrase seriously for a moment, because it reveals more than intended.

“Vibe coding” became popular because it described a real phenomenon: people generating functional code by describing what they wanted, without fully understanding the implementation. Useful shorthand. Also an admission that the person using the tool couldn’t always verify what the tool produced. The “vibe” was the part that replaced comprehension.

Extending this to “vibe working” means extending that trade-off to SEO analysis, research, project management, content strategy. You describe the vibe of what you want, the machine produces something that looks right, and you ship it. The less you understand the domain, the more you depend on the vibe. The more you depend on the vibe, the less you notice when the output is wrong.

For SEO, this is the Competence Cliff I wrote about previously, wearing product marketing lipstick. When the vibe replaces the understanding, and the review queue replaces the craft, you end up with people who can produce more but verify less. The Berkeley research tells us those same people will burn out faster while doing it. Efficient all around, really.

The competitive ratchet

There’s a mechanism in the Berkeley findings that deserves its own section because it maps onto the SEO industry like it was written about us.

When your competitor uses AI to publish 200 pages of “optimised” content a month, standing still feels like falling behind. When the agency down the road starts offering “AI visibility audits” as a service line, not offering one feels like a gap in your deck. Nobody formally raises expectations, but informal norms shift fast. Within months, what AI makes possible becomes what’s expected. What’s expected becomes what’s sold. What’s sold becomes what’s measured. And suddenly everyone’s busy with work that didn’t exist eighteen months ago and nobody’s stopped to ask whether it’s producing value or just activity.

The endgame of this logic is someone on X proudly announcing they just bought a full “Programmatic SEO” engine for $9. Nine dollars for a system that generates pages at scale. My response: buy a spam penalty for $9. But that’s where the ratchet leads: when output is cheap enough, people stop asking whether the output is worth producing. They just produce it, because not producing it feels like losing.

The SEO industry has been here before. The content farm era. The link-building volume game. Each time, the tools made it possible to scale output, the industry treated the new scale as the new standard, and the whole thing continued until Google corrected the market with an algorithm update and everyone pretended they’d known all along it was unsustainable. Same cycle. Different vocabulary. The AI tools make it possible to produce more content, monitor more surfaces, generate more reports, and track more metrics. So now everyone does. And nobody’s asking whether the additional output produces additional value, because the dashboards look fuller and the activity metrics are up and the LinkedIn posts about “10x productivity” are getting engagement.

The Berkeley researchers call the eventual result “unsustainable intensity.” SEO practitioners call it Tuesday.

What the efficiency story skips

The industry conversation about AI and SEO is stuck between two poles. Vendors selling efficiency. Practitioners worrying about replacement. Both miss what’s actually happening on the ground.

AI isn’t making SEO teams more efficient. It’s making them do more things. The per-task time dropped, but the task count exploded, and the cognitive load of managing, reviewing, and reporting on all of it is higher than before. A 2025 industry survey found that over 40% of SEO professionals said creating original content was their most time-consuming task. The industry’s proposed solution? AI content tools. More content to manage, more pages to audit, more cannibalisation risks to monitor. The time-per-article dropped. The total time spent on content went up. Nobody put that in the case study.

My read: the organisations getting this right are the ones treating AI as a way to do the same work better, not more work faster. That means being deliberate about what you automate and what you don’t. It means resisting the gravitational pull of scaling output just because scaling is now cheap. And it means saying (out loud, in a meeting, to people who don’t want to hear it) that the new tools created new work, not free time.

Don’t publish 50 AI-drafted articles just because you can. Don’t add a sixth monitoring dashboard without retiring the third. Don’t let “what’s our AI strategy?” become a quarterly reporting theatre exercise that generates decks but changes nothing.

The time you saved was real. It was never yours to keep. Your boss noticed, the scope expanded, and now you’re doing your original job plus the one the machine created. The tool got faster. You got busier. The vendors made up new vanity metrics they pinky-swear are valuable. And somewhere in a pitch deck, that’s being called “productivity.”

This is the same crowd that six months ago was posting about “AI-first teams” and “doing more with less.” Now they’re keeping the seniors. How generous. But here’s the follow-up question nobody seems to be asking: where do you think senior people come from?

They don’t appear fully formed from a LinkedIn certification. Nobody becomes a senior SEO by reading about SEO. Seniority isn’t a knowledge threshold you cross. It’s a capability built through years of doing work that, from the outside, looks like it could be automated. The industry is currently treating that work as waste to be eliminated. I think it’s the entire apprenticeship. And we’re dismantling it so we can save on headcount and call it innovation.

What “senior” actually means

Ask someone what makes a senior SEO senior and you’ll usually get an answer about knowledge. They understand technical SEO deeply. They know how Google works. They can build a strategy. If they’re feeling performative about it, they’ll mention their certifications or their “10x SEO strategist” LinkedIn headline. Maybe a webinar they hosted.

That’s the résumé version. The real answer is less flattering: a senior SEO is someone who’s been wrong enough times, in enough different contexts, to recognise what “wrong” looks like before it happens. No certification programme teaches that.

No course sells it. It’s not a credential. It’s scar tissue.

I didn’t become competent at site migrations because I studied migration checklists. I became competent because I’ve been through enough of them to know that a checklist covers (at best) about 60% of what actually goes wrong. The other 40% is contextual, unpredictable, and only recognisable if you’ve seen a version of it before. The migration where the problem wasn’t the redirects but the internal linking logic. The one where everything looked fine until somebody noticed that canonicals were pointing at the wrong URL nomenclatures two months later. The one where the client swore they hadn’t changed anything and the staging robots.txt had been live for three weeks.

None of that comes from a framework. It comes from reps.

Part of what reps give you is something no tool or best practice guide can: the judgment to know when the best practice is wrong. A junior follows the checklist because the checklist is all they have. A senior knows when to ignore it. Not out of arrogance, but because they’ve seen enough situations where the “correct” answer made things worse. They’ve watched a canonicalisation fix tank traffic because the real problem was elsewhere. They’ve seen a tool flag hundreds of critical issues on a site that was performing perfectly well. Seniority includes the confidence to look at what the tool says, look at the context, and say “no, not here.”

That confidence isn’t transferable. You can’t teach someone when to break the rules until they understand, from experience, why the rules exist in the first place.

Seniority, in any technical discipline, is pattern recognition built through repeated exposure to varied failure. You see the same category of problem (crawl inefficiency, indexing issues, ranking drops after a redesign) but each instance is different. Different stack, different business constraints, different combination of things that are broken simultaneously. Over time, you stop needing the checklist because you’ve internalised what it’s trying to protect you from. You develop what feels like intuition but is actually compressed experience.

This can’t be shortcut. You can’t read your way to pattern recognition. You can’t watch a course on it. You can’t prompt-engineer your way around it. You have to do the work, get it wrong, understand why, and do it differently next time. Repeatedly. Across years. There is no hack for this. Sorry.

The education was in the grunt work

Here’s how the pipeline used to work, before everyone decided it was inefficient.

A junior SEO would spend their first year or two doing work that nobody envied. Running crawls and reading the output line by line. Manually checking whether redirects actually landed where they were supposed to. Pulling ranking data into spreadsheets and trying to spot patterns. Tagging pages by hand. Auditing title tags across a 10,000-page site. Writing up findings that a senior colleague would review, question, and send back covered in red ink.

This work was tedious. It was time-consuming. It was, by any reasonable efficiency metric, a poor use of a human being’s time.

It was also the entire education.

You don’t learn what a healthy crawl looks like by having a tool summarise it for you. You learn it by staring at thousands of unhealthy ones until you can spot what’s off in seconds. You don’t learn what good internal linking looks like from a best practices document. You learn it by manually tracing link paths on broken sites until you understand how link equity distributes and where it leaks.

The manual, tedious, unglamorous work forced juniors to engage with the raw material of the discipline. Not the abstraction — the actual data, the actual pages, the actual behaviour of systems under real conditions. That engagement built understanding that no summary can replicate.

I’m not saying boring work is good because it’s boring. The point is that the repetitive work was where the learning happened, and we’ve started treating it as waste to be eliminated rather than education to be preserved.

What AI actually replaces

AI is very good at doing the things juniors used to do. It can crawl a site and produce a prioritised list of issues. It can audit title tags at scale. It can generate a technical SEO report from a set of inputs. It can answer “what’s the best practice for X” with reasonable accuracy. It’s fast, it’s cheap, and it doesn’t complain about being given boring work. Executives love that last part especially.

The task gets done faster, cheaper, and more consistently. A tool that audits 10,000 title tags doesn’t get tired, doesn’t miss pages, and doesn’t need onboarding. If you’re evaluating AI purely on task completion, it looks like progress. If you’re an executive whose bonus is tied to “operational efficiency,” it looks like a gift.

But the task was never the point. The task was the vehicle for learning.

When a junior manually audits those 10,000 title tags, they’re not just producing a spreadsheet. They’re absorbing context. They’re noticing that this section of the site has a completely different naming convention. They’re spotting that the blog templates are missing schema that the product pages have. They’re developing a feel for how this particular site is structured, where its patterns break down, and what the team that built it was probably thinking. That background understanding becomes the foundation for strategic thinking later.

An AI can do the audit. It can’t do the learning that the audit produces in the person doing it.

This is the distinction the industry keeps missing. “AI frees up juniors to focus on strategy” is one of those phrases that sounds smart in a keynote and falls apart under three seconds of scrutiny. Who says this? Mostly vendors selling AI tools and executives who haven’t done junior-level work since before Google had AI Overviews. Strategy isn’t a mode you switch to once the boring work is done. It’s a capability that grows out of having done the boring work so many times that you start seeing the patterns underneath it. Strategy without operational intuition is guessing with better vocabulary. But it sure looks good on a slide.

The pipeline is already thinning

This isn’t theoretical. The effects are starting to show now.

Agencies are restructuring around AI-assisted workflows that need fewer junior staff. In-house teams are automating the entry-level tasks that used to justify hiring graduates. And LinkedIn is doing what LinkedIn does best: seniors who built their entire careers on grunt work are now posting advice telling juniors they don’t need to do any. “Learn prompt engineering.” “Focus on strategic thinking.” “The future belongs to people who can work with AI.” Easy to say when your own expertise was built on exactly the manual labour you’re telling the next generation to skip. The hypocrisy is breathtaking, but at least it gets engagement.

The juniors who do get hired are being handed AI outputs to review rather than producing the analysis themselves. They’re learning to evaluate summaries instead of learning to build them. This sounds like a more efficient use of their time until you realise that evaluating an AI audit requires the same operational intuition that doing the audit manually would have built. They’re being asked to quality-check work they don’t yet have the experience to meaningfully assess.

This week, I saw someone on X post a screenshot of an AI dashboard with the caption: “The moment you realize llms.txt is more important than sitemap.xml, you win.”

Let that sink in. A sitemap.xml is a foundational web protocol that tells search engines what to crawl. An llms.txt is an unproven proposal with no established convention, no adoption standard, and no evidence that any major language model treats it as a retrieval signal. Declaring it “more important” than sitemap.xml isn’t a hot take. It’s a tell—not to say brain rot. It tells you the person’s understanding of search was built entirely on whatever their dashboard showed them that morning. When you delegate your thinking to a dashboard, you don’t win. You lose. You just don’t know it yet because the dashboard hasn’t told you.

The competence cliff

Project forward three to five years. The current cohort of seniors, the people who built their pattern recognition through years of manual work, will still be in the market. Some will have moved into leadership. Some will have left the industry. Natural attrition.

Behind them, there should be a cohort of mid-levels who’ve spent the last few years building their own operational experience, preparing to step into senior roles. But that cohort is thinner than it should be, because the work that would have developed them has been automated or eliminated. They have tool proficiency. They have framework knowledge. They have, in many cases, genuinely good strategic instincts. What they lack is the accumulated reps — the dozens of migrations, the hundreds of audits, the thousands of individual problems encountered and resolved — that turn good instincts into reliable judgment.

When organisations try to hire senior practitioners to replace the ones who’ve left, they’ll find the pool is smaller than expected. Not because people left the industry, but because fewer people were developed into seniors in the first place. The pipeline wasn’t cut off — it was narrowed, quietly, one automated task at a time.

And the organisations that narrowed it will be the same ones wondering why they can’t find experienced hires. They’ll blame the “talent shortage.” They always do. It’s never “we destroyed the pipeline.” It’s always “the market is tough.”

“But AI makes everyone more productive”

The strongest counterargument goes like this: AI doesn’t eliminate learning, it accelerates it. Juniors can use AI to handle the mechanical parts and spend more time on the analytical parts. They’ll learn faster, not slower, because they’re not wasting time on repetitive tasks.

This is the argument people make when they’ve already decided to cut headcount and need a narrative that makes it sound like a favour to the people they didn’t fire. I find it unconvincing for a specific reason: it assumes you can separate the mechanical from the analytical, and that the mechanical part has no educational value. In practice, the boundary between “tedious task” and “learning experience” is invisible at the time. You don’t know which crawl review will be the one where you notice the pattern that shapes how you think about crawl efficiency for the next decade. You can’t predict which manual audit will be the one where something unexpected teaches you how a specific CMS handles canonical tags. The insight emerges from the repetition. Remove the repetition, and you remove the conditions for the insight to occur.

There’s also a selection bias that nobody in the “AI accelerates learning” camp wants to acknowledge. The people saying “AI would have made me learn faster” are people who already learned the hard way. They’re projecting backwards from a position of existing expertise, imagining how they’d use AI with the judgment they already have. A junior doesn’t have that judgment yet. That’s the whole problem. It’s like a chef saying a food processor would have sped up their training. Maybe. But you still need to learn what properly diced onions look like before the machine is useful. You can’t skip to “efficient” without going through “competent” first.

Can AI be a useful learning tool? Sure. In the same way that having a knowledgeable colleague explain something is useful. But it’s a supplement to doing the work, not a replacement for it. And the current industry trend isn’t “use AI to learn faster.” It’s “use AI to do the work instead of hiring someone to learn by doing it.” Those are very different things, and the people conflating them usually have a tool to sell or a headcount reduction to justify. Often both.

What this means for practitioners

If you’re early in your career: do the work manually, even when you don’t have to. Run the crawl yourself before asking the AI to summarise it. Build the spreadsheet before using the automated report. The tool will always be there when you need efficiency. The operational understanding you build by doing it the slow way compounds over your entire career. Ignore the LinkedIn seniors telling you to skip the part they didn’t skip. They got theirs. They’re not thinking about whether you’ll get yours.

If you’re mid-career: you’re in the last generation that got the full apprenticeship. That’s an asset, and it’s becoming a rare one. The pattern recognition you built isn’t replicable by tools, and it’s going to become scarcer. Your value isn’t in executing tasks faster. AI will always beat you there. Your value is in knowing which tasks matter, which outputs to distrust, and what the tool can’t see. Protect that. It’s worth more than whatever certification is trending this quarter.

What this means for the people making decisions

If you’re running a team or an agency: every junior role you eliminate or automate is a senior hire you won’t be able to make in five years. The savings are real and immediate. The cost is deferred and invisible — until it isn’t. Which, conveniently, means you’ll probably have moved on to a different role before anyone connects the cause to the effect. Very tidy.

The question isn’t whether AI can do the work juniors used to do. It can. The question is whether you have a plan for developing the judgment that doing that work used to create. If the answer is “they’ll figure it out” or “we’ll hire experienced people,” you’re banking on a pipeline that you’re actively draining. “We’ll hire experienced people” only works when someone, somewhere, is still developing them. If every organisation adopts the same strategy of automating junior work and hiring senior talent, the maths stops working. You can’t all fish from the same shrinking pond and act surprised when it’s empty.

Nobody’s going to notice the cliff until they’re standing at the edge. By then, the people who would have been your senior hires will be mid-levels with five years of tool proficiency and two years’ worth of actual reps.

Seniority isn’t a title you earn by surviving long enough. It’s a capability you build by doing the work. Repeatedly, imperfectly, and in enough different contexts to recognise the next problem before it fully arrives. There’s no shortcut. There never was. And the industry is about to learn that the hard way.

“Forget about SEO blogs and resources.”

They expect a reading list. A curated collection of industry newsletters, maybe a podcast recommendation, possibly a course with a certificate they can add to LinkedIn. Instead I tell them to read a book about usability from 2000 and study accessibility guidelines that predate Google’s IPO.

This isn’t gatekeeping. It’s directions to the actual gate.

The SEO content problem

The SEO industry has a content problem, which is ironic given that content is supposedly what we’re experts at optimising.

Most SEO resources exist to generate traffic for SEO agencies. They’re written to rank, not to teach. The incentive structure guarantees that what you’re reading is optimised for impressions, not for making you competent. Every “complete guide to SEO in 2026” is really a “complete guide to what we think will get us leads in 2026.”

This creates a bizarre feedback loop. People learn SEO from content designed to demonstrate SEO, which teaches them that SEO is about creating content designed to demonstrate SEO. Somewhere along the way, the actual discipline—making websites work well for humans and machines—gets lost in the recursion.

And then there’s the freshness treadmill. SEO blogs have to keep publishing. Google makes an announcement, and suddenly everyone needs a hot take. Half of what gets written is speculation dressed as analysis, repackaged within 48 hours as “what we know so far” and within a week as “the definitive guide.” By the time something is definitive, Google has moved on and we’re speculating about the next thing.

You can spend years reading SEO content and come away knowing a lot about what Google said in various blog posts but very little about why websites work or don’t work.

What to learn instead

The disciplines that actually matter predate SEO and will outlive whatever Google does next. They fall into three categories:

1. how the web works

2. how humans work

3. how to think about problems

SEO is supposed to be the intersection of all three, but the industry spends most of its time ignoring the foundations.

How the web works

Web development best practices. Not “technical SEO”—actual web development. How browsers render pages. How servers respond to requests. What happens between a click and a painted screen. The performance work Google’s been pushing for years (Core Web Vitals, mobile-first indexing, page experience signals) is just web development best practices rebranded with a ranking incentive attached. If you understand how the web works, you understand what Google is measuring without needing someone to tell you.

Web Accessibility. The Web Content Accessibility Guidelines exist because the web should work for everyone. Following them makes your site work better for screen readers, keyboard navigation, and—surprise—search engine crawlers. The overlap isn’t coincidental. Crawlers are, functionally, blind users who can’t execute JavaScript reliably. When accessibility advocates spent decades fighting for semantic HTML, they were also accidentally building the foundation for technical SEO. You’re welcome.

Information retrieval. This is the academic discipline search engines are built on. Precision versus recall. Relevance ranking. Query understanding. The trade-offs between returning exactly what someone asked for and returning what they probably meant. You don’t need a computer science degree, but understanding these concepts at a basic level explains why Google makes certain decisions that otherwise seem arbitrary. When Google “fails” to rank your page, they’re usually making a retrieval trade-off you haven’t considered.

How humans work

Usability. Steve Krug’s Don’t Make Me Think was published in 2000. It’s still the best introduction to how people actually use websites, as opposed to how we imagine they use them. The core insight—that users don’t read, they scan; they don’t make optimal choices, they satisfice; they don’t figure out how things work, they muddle through—explains more about bounce rates and engagement signals than any heatmap tool.

Jakob Nielsen’s heuristics. Ten principles from 1994 that explain why most websites fail. Visibility of system status. Match between system and real world. User control and freedom. These aren’t SEO concepts, but they explain why some sites convert and others don’t. And since Google has spent twenty years trying to measure whether sites are actually good, understanding what “good” means tends to help.

Information Architecture. How content should be organised so humans can find what they need. This isn’t about siloing keywords or building topical authority—it’s about understanding how people categorise information and navigate structures. The card sorting studies from the 1990s are more useful for site structure decisions than any “internal linking strategy” post written last month.

Content design. Sarah Winters built the content discipline at GOV.UK and wrote the book on it—literally. Content design asks “what does the user need to do?” before “what should we write?” It treats content as problem-solving, not publishing. This is the opposite of how most SEO content gets produced, where the question is “what keywords should we target?” and the user’s actual needs are reverse-engineered from search volume.

How think about problems

Findability. Here’s the irony: there’s already a discipline that describes what SEO is supposed to do, and it’s not called SEO. Peter Morville’s Ambient Findability (2005) frames the problem of “how do people find things?” as a design challenge spanning search, navigation, wayfinding, and information architecture. Findability is SEO with the Google-specific tactics stripped out—the actual problem, not one platform’s implementation of it. When you understand findability as a discipline, you stop treating search engines as the centre of the universe and start treating them as one channel among many.

Strategic thinking. Most SEO “strategies” aren’t strategies at all. Richard Rumelt’s Good Strategy/Bad Strategy provides the clearest framework for understanding the difference. A strategy has a diagnosis (what’s actually going on), a guiding policy (the approach you’ll take), and coherent actions (specific steps that reinforce each other). “Rank higher for commercial keywords” is a goal. “Build more backlinks” is a tactic. Neither is a strategy, and confusing them is why so much SEO work feels like activity without progress.

This matters because SEO decisions don’t happen in isolation. They compete for resources with other priorities. If you can’t articulate why a technical fix matters more than a new feature, or why content investment should come before link building, you’re not doing strategy—you’re doing advocacy for your department. Rumelt teaches you to diagnose the actual situation, not the situation you wish you had.

Why this works

Google’s stated goal is to surface the best results for users. You can argue about how well they achieve it, but the goal itself points in a direction: make good websites.

The problem with learning SEO from SEO resources is that you learn to optimise for the measurement instead of the thing being measured. You learn to game signals rather than create the quality the signals are trying to detect. This works until it doesn’t, and then you need a new tactic.

The foundational disciplines don’t have this problem. Usability principles don’t change when Google updates an algorithm. Accessibility requirements don’t pivot based on what’s ranking. Information architecture isn’t subject to speculation about what the September core update might have targeted.

When you understand why Google values something (because it indicates quality to users) you can reason from first principles about any change they make. When you only know that Google values something, you’re stuck waiting for someone to tell you what to do next.

This is why people who understood usability, accessibility, and content design never needed to be told about E-E-A-T. When Google published guidelines about demonstrating experience, expertise, authoritativeness, and trustworthiness, it wasn’t news—it was a description of what they’d been doing all along. The SEO industry treated it as a new ranking factor to optimise for. Everyone else recognised it as table stakes.

This is also what separates experienced SEOs from people who’ve simply done SEO for a long time. Seniority isn’t years served—it’s variants of the same problem solved across different contexts. A senior SEO has seen the same underlying issue manifest in enterprise e-commerce, in media publishing, in SaaS documentation, and recognises the mechanism even when the surface symptoms look nothing alike. That pattern recognition doesn’t come from checklists. It comes from understanding why things break.

Courses and certifications can teach you what to do. They can’t teach you what to do when the standard answer doesn’t apply—which is most of the time, if you’re working on anything interesting. You can memorise every best practice in the industry and still be useless the moment something breaks in a way the checklist didn’t anticipate. The people who get stuck are the ones who learned what to do without ever understanding why it works.

This has always been true. What’s changed is the cost of not understanding.

AI can execute checklists faster and more consistently than any human. If your value proposition is following documented procedures, running through audit templates, chasing ranking tricks; you’re competing with something that doesn’t sleep and doesn’t bill by the hour. The button-pushers and checklist-followers are already being replaced. Not by some future technology, but by tools that exist today.

What AI still struggles with is reasoning through novel problems. It can’t look at a site losing traffic and figure out which of twelve possible explanations actually applies to this specific situation, given this specific business context, with these specific constraints. That requires understanding mechanisms, not memorising outputs.

That’s the job. That’s always been the job. The difference is that now the alternative is obsolete, not just less effective.

The uncomfortable implication

If the best SEO education comes from UX, accessibility, and web development, what exactly is the SEO industry contributing?

Coordination, mostly. Someone needs to translate between disciplines, prioritise work based on impact, and make the business case for technical improvements that would otherwise languish in backlogs. There’s value in that.

But the knowledge itself? The foundational understanding of how to build good websites? That comes from people who weren’t trying to rank anything. They were trying to make the web work.

The SEO industry took those principles, repackaged them with keyword-focused framing, and sold them back at markup. Information Architecture became “content silos.” Web Development’s Graceful Degradation became “JavaScrip SEO.” Usability became “page experience optimisation.”

I’m not saying there’s no original thinking in SEO. There is. But the core competencies—the stuff that actually matters when the algorithms change and the tactics expire—came from somewhere else.

If you want to learn SEO, go learn that instead.

This is why my own website’s knowledge base lists foundational books—Krug, Rumelt, the IR textbook—before any “Learn SEO” resources. The order isn’t accidental.

Reading list (none of it about SEO):

Transparency Note: If you buy through the links below, I may earn a small commission at no extra cost to you. This helps support my work—thank you!

How the web works:

• High Performance Web Sites — Steve Souders

• Web Content Accessibility Guidelines (WCAG) — W3C

• Introduction to Information Retrieval — Manning, Raghavan, and Schütze (free online)

How humans work:

• Don’t Make Me Think — Steve Krug

• 10 Usability Heuristics for User Interface Design — Jakob Nielsen

• Information Architecture for the World Wide Web — Rosenfeld and Morville

• Content Design — Sarah Winters

• Designing with the Mind in Mind — Jeff Johnson

How to think about problems:

• Ambient Findability — Peter Morville

• Good Strategy/Bad Strategy — Richard Rumelt

When you’ve finished those, you’ll understand more about what makes websites rank than most people who’ve spent years reading SEO blogs.

That’s not an insult to SEO blogs. It’s a reflection of what they’re optimised for—and it isn’t teaching you.

Meanwhile, IBM understood something forty-seven years ago:

“A computer can never be held accountable, therefore a computer must never make a management decision.” — IBM Training Manual, 1979

Forty-seven years ago, IBM understood something that’s been memory-holed in the current rush to replace jobs with API calls: accountability requires agency. Agency requires the capacity to be held responsible.

The principle wasn’t controversial then. It shouldn’t be controversial now.

And yet.

My LinkedIn feed has become a rolling obituary. Not of tasks eliminated, but of roles. Entire positions replaced by AI systems that cannot be fired, disciplined, questioned in a post-mortem, or asked what they were thinking.

They weren’t thinking. They were predicting the next token.

This isn’t an argument against AI. It’s an argument against a specific flavour of organisational negligence that’s being dressed up as innovation.

The data bears this out. McKinsey’s latest numbers show 80% of companies have deployed generative AI in some form; and roughly 80% report no material impact. Nine in ten function-specific use cases remain stuck in pilot mode. That’s not “early days.” That’s a pattern.

Tasks vs. roles

There’s a distinction being ignored in most AI deployment conversations: the difference between automating tasks and eliminating roles.

Automating tasks is sensible. If AI can summarise documents, generate first drafts, process routine requests, or surface relevant information faster than a human—good. Use it. That’s what tools are for.

Eliminating roles assumes something more aggressive: that the role was those tasks. That nothing remains once you subtract the automatable components.

This is almost never true for roles involving judgement.

A content strategist doesn’t just produce content. They recognise when the brief is wrong. They know which stakeholder will object and why. They remember what was tried two years ago and why it failed. They notice when the data says one thing but the situation suggests another.

An editor doesn’t just fix grammar. They understand what the publication is for, what the audience will tolerate, when a piece is technically correct but editorially wrong.

A junior analyst doesn’t just run reports. They’re learning the organisation; building the context and judgement that will eventually make them senior, make them a manager, make them someone capable of decisions that can’t be automated.

Eliminate the role and you don’t just lose the tasks. You lose institutional knowledge, contextual judgement, pattern recognition that comes from being embedded in the work. You lose the ability to notice when something is off before it becomes a problem.

And you lose the training ground for people who would eventually make the decisions you still need humans to make.

The reliability curve

This isn’t just about what feels right; it’s about what actually works. Recent data from Anthropic’s Economic Index paints a stark picture of what happens when you mistake AI capabilities for human role replacement.

Success rates drop as complexity rises. Reliability for “college-degree-level” tasks sits at around 66%, dropping further as tasks get longer. For complex, multi-step operations (precisely the kind of work a specialised role entails) failure rates approach a coin flip.

Eliminating a role assumes the AI can handle the complexity of that role. The data shows that complexity is exactly where performance breaks down. You aren’t replacing a person with a cheaper equivalent; you’re replacing a reliable agent with one that fails half the time on the work that actually matters.

The mathematics are worse than you think. Luca Rossi recently calculated that even at an optimistic 95% reliability per step, a 20-step workflow succeeds only 36% of the time. Compounding failure rates don’t compound nicely.

This explains the demo-to-production gap. Every AI demo is a carefully curated three-to-five-step performance on the happy path. Real roles involve dozens of interdependent decisions, edge cases, and judgement calls about when the process itself is wrong. The gap between demo and deployment isn’t a bug being fixed—it’s where human judgement lives.

The accountability gap

Here’s the question that doesn’t appear in ROI calculations: when the AI makes a decision that turns out to be wrong, who gets held responsible?

Not “who takes the blame” in some abstract reputational sense. Who sits in the meeting and explains what happened? Who gets performance-managed? Who loses their bonus, their role, their job?

If the answer is “no one, really,” then you’ve created an accountability vacuum. Accountability vacuums are where organisational dysfunction breeds.

The AI vendor isn’t accountable—read your contract. They’ve disclaimed responsibility for outputs with admirable thoroughness. The AI itself isn’t accountable—it has no capacity for consequence. The executive who approved the deployment points to the business case. The manager who implemented it points to the executive’s directive. The remaining team members point out they weren’t consulted.

Nobody is responsible. Which means nobody is positioned to fix it.

The invisible risk

This isn’t just about bad decisions; it’s about invisible actions. As PromptArmor recently demonstrated, “agentic” tools can be manipulated to exfiltrate sensitive files via indirect prompt injection; reading a malicious file and sending your data to an attacker.

The chilling part of their finding? “At no point in this process is human approval required.”

When you remove the human from the loop to “reduce friction,” you also remove the only entity capable of noticing that something is wrong before the data leaves the building. The risk is an “agentic blast radius” that remains invisible until it’s catastrophic.

This isn’t theoretical. Organisations learn from mistakes through accountability. Someone has to own the error, understand what went wrong, and have both the authority and incentive to prevent recurrence. When you remove humans from decision points, you remove the mechanism by which organisations self-correct.

You also remove the people who would have caught the error before it became a mistake. Human oversight is invisible when it works. The decisions not made, the outputs not published, the recommendations not followed because someone with judgement intervened. None of that shows up in a productivity metric. You won’t know it was valuable until it’s gone and something breaks.

The optimisation trap

AI is very good at optimising for defined metrics. Give it a clear objective and a feedback mechanism, and it will pursue that objective relentlessly.

This is useful when the metric accurately captures what you want.

It’s dangerous when it doesn’t.

Humans are reasonably good at recognising when the metric itself is wrong; when optimising for it creates perverse outcomes, when the situation has changed and old success criteria no longer apply, when the letter of the objective conflicts with its spirit.

AI has no access to the spirit. It has the metric. It will optimise for the metric. If the metric is wrong, it will optimise for the wrong thing with impressive efficiency.

This is why AI works well for closed problems: clear inputs, clear success criteria, limited scope for judgement about whether the criteria themselves are correct. It works badly for open problems, where the hardest part isn’t executing against the objective; it’s determining what the objective should be.

Most interesting business decisions are open problems. Pretending they’re closed because you want to automate them doesn’t make them closed. It means you’ve automated the wrong thing.

The autonomy paradox

There’s a revealing pattern in how different economies are adopting these tools. Sophisticated, high-income economies are actually granting AI less autonomy, not more. They use it collaboratively; augmenting human judgement rather than replacing it.

Conversely, regions with less developed digital infrastructure are more likely to attempt full delegation of tasks.

The “smart money” isn’t firing people to let agents run wild. It’s using agents to make smart people faster. Fully delegating roles to AI isn’t the strategy of mature innovators; it’s a misunderstanding of the technology’s optimal operating point.

Before you eliminate a role

If you’re considering replacing a role with AI, ask these questions. If you can’t answer them clearly, you’re not ready.

Task or judgement? Are you automating discrete, repeatable tasks, or eliminating the judgement that determines when and how those tasks should be performed? If the role involves deciding what to do, not just how to do it, you’re eliminating judgement.

Who’s accountable? When this AI system makes a consequential error, who specifically will be held responsible? Not “the team” or “the process”. Which individual person will own the failure and have authority to fix it? If you can’t name someone, you’re creating a vacuum.

How will you know when it’s wrong? Human oversight catches errors through judgement and context. If you’re removing the humans, what’s your detection mechanism? “We’ll monitor the outputs” isn’t a plan unless you’ve specified who monitors, what they’re looking for, and what authority they have to intervene.

What does this role prevent? What mistakes don’t happen because someone with judgement is in the loop? This is hard to quantify, which is why it’s ignored in business cases. Ignoring it doesn’t make it zero.

Where does that knowledge live? What context and history exists in the people you’re removing? How will it be preserved? “It’s in the documentation” is almost never true. The most valuable knowledge is precisely the stuff that didn’t get documented because it lived in someone’s head.

Where do senior people come from? If you eliminate junior roles, where do experienced people come from? If your plan assumes you can always hire judgement from outside, you’re assuming everyone else will keep training people you can poach. That’s not a strategy. It’s a dependency on other organisations being less short-sighted than you.

Augmentation, not replacement

None of this is an argument against AI. It’s an argument against mistaking “we can automate this” for “we should eliminate the human who does this.”

The organisations getting results aren’t eliminating human oversight—they’re calibrating it. Rossi’s analysis found that successful deployments are “selectively autonomous”: independent on routine tasks, supervised on critical ones. The word “selectively” is doing all the work in that sentence.

This isn’t a temporary limitation waiting for better models. It’s recognition that the value of human judgement isn’t the tasks themselves; it’s knowing when the tasks shouldn’t be done, when the objective is wrong, when the process needs to break.

The executives currently eliminating roles rather than augmenting them aren’t innovating. They’re creating organisational debt that will come due when something breaks and there’s no one left who understands why it was built that way.

IBM understood this in 1979. The question is whether you’ll understand it before or after you’ve dismantled the judgement layer that was keeping things functional.

Strip away the agentic framing, and UCP is fundamentally about structured data. Again.

It’s not that UCP doesn’t solve real technical challenges—it does. It establishes a standardised way for AI systems to interact with product catalogues, inventory, and checkout flows without scraping HTML or guessing which <div> contains the price. For retailers who’ve actually maintained quality product feeds, UCP extends capabilities they’ve already been building toward.

But the more interesting story isn’t what UCP enables technically. It’s what Google’s learned about motivating compliance with requirements that have existed for years.

The Technical Reality: This Isn’t New (-sflash!)

UCP might be new as a formal protocol, but the concept isn’t. Google’s allowed brands to use the checkout_link_template field in Merchant Center to send users directly to checkout from search results since April 2025, with Shopify testing beginning in 2023. If you’ve already implemented this, UCP is the formalised wrapper around something you’ve been incrementally building toward.

The requirements underlying UCP—clean product data, accurate inventory, proper image specifications, operational details about shipping and returns—are identical to what’s been necessary for Merchant Center compliance all along. The fields merchants need to populate aren’t new. The organisational capability required to maintain that data hasn’t changed. The cross-departmental coordination between inventory management, marketing, and operations remains exactly as complex as it was before “agentic commerce” became the framing.

What has changed is how seriously retailers are taking these requirements.

The Motivation Problem Google Solved

Turns out the secret to getting retailers to care about structured data wasn’t better documentation; it was calling it “agentic commerce” and letting FOMO do the rest.

Google’s spent years publishing guidelines on feed quality, offering Top Quality Store programmes, and providing dashboards showing exactly where merchant data falls short. Adoption remained patchy. Feed quality issues that directly impacted visibility in Shopping results were treated as “nice to have” optimisation work rather than foundational business requirements.

Rebrand the same compliance requirements as “preparation for AI-driven commerce,” and suddenly data governance becomes a board-level priority. The underlying ask hasn’t changed—populate your product catalogue with accurate, comprehensive, structured information. But frame it as “you’ll be invisible to AI agents” rather than “you’ll rank poorly in Shopping,” and the urgency shifts entirely.

It’s brilliant positioning, honestly. The AI hype cycle is doing the motivational work that years of documentation couldn’t achieve.

Which would be fine—genuinely useful, even—if the shopping behaviour UCP enables actually existed at meaningful scale.

The Behaviour That Isn’t There

Google’s announcement assumes a world where consumers routinely delegate purchase decisions to AI agents. The technical infrastructure supports it. The structured data makes it possible. The protocol standardises it.

But do people actually want agents making these decisions on their behalf?

Amazon’s Alexa has been sitting in homes for years, periodically nagging about “items in your cart.” It’s used primarily for reordering things you already know you want—the same laundry detergent, the same coffee pods, the same predictable replenishment purchases. For anything requiring evaluation, visual assessment, or comparison, the voice interface fails not because of technical limitations but because humans prefer to look at things before buying them.

This isn’t speculation. Academic research on voice commerce adoption consistently shows that consumers exhibit what researchers call “algorithm aversion“ for subjective purchase decisions. They’ll trust an AI to calculate the fastest route or find the cheapest flight—tasks involving quantifiable optimisation—but actively resist algorithmic recommendations for anything involving taste, aesthetics, or personal preference. The reasoning is intuitive: consumers believe an entity that cannot experience a product cannot adequately recommend it.

There’s a reason Amazon’s product pages are dominated by user-generated photos rather than just manufacturer specifications. Structured data can tell you a camping chair supports 300 pounds and uses recycled materials. But consumers don’t trust structured data alone; they want visual verification from other humans who’ve actually used the product. They want to see how it looks in someone’s garden, how it folds, whether the fabric looks cheap or substantial.

This preference for visual validation isn’t a quirk—it’s a fundamental cognitive pattern. Research on screenless interfaces shows that consumers use voice to add items to a cart but wait to checkout until they can verify the item on a screen. The voice interface captures intent; the visual interface closes the transaction. Users aren’t abandoning voice because the technology fails—they’re abandoning it because seeing the product before committing feels non-negotiable.

Retailers are being asked to provide extensive structured data so AI agents can confidently recommend products that humans will still want to see pictures of before buying.

The confidence gap isn’t technical. No amount of protocol standardisation solves “I don’t trust an agent to pick the right camping chair for me without seeing it first.” That’s a human decision-making preference, not a data availability problem. UCP solves the technical challenge of agent-driven commerce whilst assuming away the behavioural challenge of whether anyone wants it for purchases beyond routine replenishment.

The Delegation Hierarchy Problem

Even if you accept that some purchases will happen through agents, the research suggests a clear hierarchy of what consumers will actually delegate.

Studies on AI-assisted purchase decisions consistently find the same pattern: consumers are comfortable delegating information retrieval (”What’s the price of X?”) and commodity reordering (”Reorder toothpaste”). They’re moderately willing to accept curation for low-stakes discoveries (”Find me a mystery novel”). They actively resist delegating decisions involving personal taste, social signalling, or emotional significance (”Buy a birthday gift for Mum”).

The higher the personal stakes, the stronger the resistance. Research on financial delegation to algorithms shows that consumers hold AI to a perfection standard they don’t apply to humans. If a friend recommends a terrible book, you forgive them. If an algorithm does it, you question the entire system’s competence. This asymmetric tolerance for error makes consumers reluctant to hand over decisions where getting it wrong carries consequences.

Google’s UCP infrastructure is built for comprehensive agent commerce. The actual consumer appetite is for agent-assisted commodity replenishment. That’s a meaningful gap between the protocol’s ambition and the behaviour it assumes.

The Measurement Problem You’re Not Being Told About

Even if agent-driven discovery does happen at scale, you’ll struggle to measure it properly.

If checkout happens within Google’s surfaces rather than on your site, your attribution breaks in new ways. The same transaction reports differently in GA4. Traffic that previously showed as organic search landing on product pages now shows as... what, exactly? Direct checkout completions with minimal session data? The “open protocol” language obscures a practical reality: the customer journey becomes less visible to you whilst remaining entirely visible to Google.

One merchant noted that traffic using checkout_link_template already bypasses traditional product page analytics. You can filter GA4 by checkout URL to see this traffic, but it’s a workaround, not a solution. With UCP, expect this measurement gap to widen. Google’s documentation doesn’t clarify how UCP transactions will appear in your analytics, which suggests you’ll be optimising for a channel with limited visibility into performance.

This is “AI optimisation is repackaged SEO with worse measurement“ made concrete. You’re being asked to invest in structured data compliance for a shopping paradigm that may not materialise at scale, and if it does, you’ll have reduced visibility into how it’s performing.

What Actually Matters Here

If you’re going to invest in UCP compliance—and if you want to maintain visibility in Google’s shopping surfaces, you probably should—do it because structured data quality was already table stakes for discovery, not because you believe agents will drive significant purchase volume.

The fundamentals haven’t changed:

Feed quality still determines visibility. Whether it’s called “optimising for Google Shopping” or “preparing for agentic commerce,” the requirement is identical: accurate, comprehensive product data maintained consistently across your catalogue.

Organisational capability remains the bottleneck. If your team couldn’t maintain clean Merchant Center feeds before UCP, the new protocol won’t suddenly make cross-departmental data governance easier. This requires coordination between inventory management, marketing, operations, and customer service, not just technical implementation.

Visual evaluation still drives purchase confidence. For anything beyond replenishment, consumers want to see products, compare options, and verify claims through user-generated content. Agent recommendations might narrow the consideration set, but purchase decisions still require human assessment. The research is consistent on this: consumers use voice and agents for the search phase of the funnel but require visual interfaces for the buy phase.

Measurement will get messier, not cleaner. Plan for reduced visibility into how transactions happen, even as you’re expected to optimise for the channel. The protocol being “open” doesn’t mean the customer journey data is accessible to you.

The one genuinely useful outcome of Google’s UCP announcement: if the AI hype finally motivates your organisation to treat structured data as a foundational capability rather than a tactical afterthought, that’s valuable regardless of whether agentic commerce happens at scale. Clean product data improves discovery across every channel, not just hypothetical AI agents.

Just don’t mistake protocol infrastructure for evidence of consumer demand. Google’s built the technical foundation for agent-driven commerce. Whether anyone actually wants to shop that way remains an open question—one that won’t be answered by how many retailers implement the protocol, but by whether consumers ever trust agents to make purchase decisions beyond reordering the same laundry detergent.

The vocabulary has changed. The fundamental challenge (getting retailers to maintain quality structured data) remains exactly what it’s always been. If calling it “agentic commerce” finally gets organisations to prioritise it, perhaps the rebrand was worth it after all.

It’s a fair question. It’s also the wrong framing.

The transaction was always asymmetric

The implicit bargain of web content—”I let you index my work, you send me traffic”—was never actually a contract. It was a side effect of how retrieval worked. Search engines needed to send users somewhere because they couldn’t answer questions directly. That barrier is getting demolished.

What we’re witnessing isn’t theft. It’s the end of a lucky externality that content producers mistook for a guaranteed exchange. Sorry, but nobody signed anything.

You can’t retrieve what’s been absorbed

Once your content enters an AI system’s training data, it doesn’t exist there as a discrete, citable source. It becomes part of the model’s general capability—diffused, recombined, fundamentally transformed. This is mostly irreversible, and there’s no file to link back to. The original piece has been metabolised into something that no longer depends on its source.

This isn’t a bug that better citation systems will fix. It’s the architecture.

Current AI systems often can’t tell you where specific knowledge originated because that knowledge has been stripped of provenance during training. Asking for attribution from these systems is like asking someone to cite which conversations shaped their personality. Good luck with that invoice.

The degree to which retrieval-augmented generation (RAG) systems might address this in specific contexts remains unclear—they can cite retrieved documents, but that’s a fundamentally different mechanism from attributing absorbed training knowledge. Don’t hold your breath waiting for a technical fix that makes everyone whole.

The strategic question you should actually be asking

The conversations I keep having assume content production remains the core activity worth protecting. But I think the strategic lens needs adjusting: what content should you produce at all, and for whom?

If your content exists primarily to rank for keywords and capture traffic, you’re competing in a game whose rules have already shifted. AI systems can produce adequate keyword-targeted content faster and cheaper than you can. The transaction where you’d hire a writer for quick, serviceable content now routes to AI instead. The junior copywriter’s competition isn’t another junior copywriter anymore—it’s a subscription that costs less than their lunch.

The content worth producing is the work AI cannot do:

• Research that requires synthesis across non-public sources — conversations, proprietary data, original investigation

• Expertise that compounds — deep specialisation that builds on itself in ways generic models can’t replicate

• Perspectives that carry risk — positions that require judgment, stake, and accountability

Everything else is increasingly commodity. Fighting for attribution on commodity content is fighting for table scraps. And the table scraps aren’t even that good.

Walled gardens aren’t the answer either

Some conclude that paywalls have already won. That’s one outcome. But it’s worth noting what you lose behind a wall: discoverability, influence, the ability to shape how your expertise is understood in the broader ecosystem. Congratulations, you’ve protected your content. Nobody’s reading it, but it’s safe.

I think the more useful framing is this: be thoughtful about what you give away for free. Not everything. Not nothing. Strategic disclosure that establishes authority without surrendering your full value proposition.

This means:

• Publishing insights that demonstrate capability without revealing methodology

• Creating content that generates demand for expertise rather than satisfying it completely

• Treating public content as marketing for services, not as the product itself

If you’re putting out everything of value you have, eventually you run out. And then what? You can argue that you’ll keep doing research, keep generating new insights—and if you can sustain that, brilliant. But most businesses can’t maintain that pace indefinitely. Racing to publish every insight before competitors do starts looking like a race to the bottom. A race nobody asked you to enter, by the way.

The specialisation imperative

The consistent thread through this shift: generalists producing general content for general audiences face the biggest challenges. The path forward is specificity.

I’ve learned more from people who aren’t visible on social media than from most of the names that dominate the feeds. Deep expertise doesn’t require a platform. But increasingly, a platform without deep expertise is just noise. Loud, well-marketed noise—but noise.

Become the person who knows a domain so thoroughly that AI systems would need to train specifically on your work to replicate your capability—and even then would lack the ongoing judgment and contextual awareness you bring. Be the person others think of when they encounter a specific problem. Not “someone who writes about SEO,” but “the person you call when your indexing is inexplicably broken and you’ve tried everything.”

This isn’t comfortable advice for people who’ve built careers on volume and breadth. But the alternative—demanding that AI systems develop attribution mechanisms they’re not architected to provide—is hoping for a solution that may never arrive. You might be waiting a while.

What actually changes

Stop expecting reciprocity from systems that don’t operate on reciprocity. Start treating content strategy as a subset of business strategy rather than the other way around.

The question isn’t “how do we force AI to cite us?” It’s “what do we produce that remains valuable regardless of whether AI cites us?”

That’s a harder question. It’s also the only one worth spending time on.

Teams experimented with prompts, dashboards, and new acronyms. AI felt like a new surface that must be “optimised for”—quickly, before someone else figured it out first. Most of this activity was driven by uncertainty rather than evidence. It still is.

(The conference circuit did very well out of it, though.)

That phase will fade. AI stops being novel and starts being ambient. The tools don’t disappear—the anxiety around them does. And once that happens, a quieter truth becomes obvious: the fundamentals that determine whether information is found, trusted, and reused haven’t actually changed.

From experimentation to default behaviour

Right now, AI sits beside existing workflows. People ask it questions they used to type into Google. They test how often their brand appears in answers. They screenshot wins.

But default behaviour looks different from experimentation. When AI becomes infrastructure, users stop exploring and start delegating. Queries get shorter, more decisive, and less forgiving. The intent shifts from “show me options” to “just tell me.”

That change doesn’t reward clever optimisation tricks. It rewards clarity.

When machines have to choose what to retrieve, summarise, or cite, ambiguity becomes a liability. Content that tries to do too many things at once quietly disappears. Content that explains one thing well, from a clear point of view, gets reused—not because it’s clever, but because it’s dependable.

AI-generated answers don’t create a new search system

A lot of the “AI Optimisation” discussion assumes we’re dealing with a new ranking logic. In practice, most of the predictable behaviour we see in AI-generated answers comes from retrieval, not generation.

When an AI system needs to ground an answer in external information, it has to retrieve that information first. That retrieval step looks very familiar: indexing, semantic matching, relevance scoring. If content shows up consistently in grounded answers, it’s usually because it satisfies the same conditions that made it discoverable in search engines to begin with.

What confuses people is everything outside that predictable layer.

Ungrounded AI output can mention brands, URLs, or ideas that have little or no search visibility. Those moments feel exciting, but they’re not reproducible. They’re the side effect of probabilistic generation, not evidence of a new authority model. Treating them as strategy is a category error.

It’s also a great way to sell tools.

The future belongs to what’s influenceable, not what’s occasionally visible.

The technical distinction between grounded retrieval and probabilistic generation—and why only one of them is influenceable—is explored in more detail in Visibility in LLMs and AI Overviews.

Content strategy after content becomes cheap

As AI lowers the cost of producing text, it raises the cost of being worth reading—or reusing.

This is where human insight becomes the real differentiator. Not “human tone” or “authentic voice” in the abstract, but actual judgment: what to include, what to exclude, and what you’re willing to stand behind.

Signals like experience, expertise, and credibility matter less because algorithms “care” about them, and more because retrieval systems bias toward sources that are already trusted, well-defined, and internally consistent. Vague content can be generated infinitely. Specific content has friction. That friction is what makes it valuable.

In an AI-saturated web, the winning strategy isn’t to publish more. It’s to publish less, with more conviction.

The strategic shift most teams are missing

The biggest opportunity created by AI isn’t tactical. It’s organisational.

Most companies still treat discoverability as a downstream activity. Something to fix after launch. Something owned by a single team.

That model doesn’t scale when machines, not just humans, are your primary audience.

Teams that perform well in AI-driven discovery environments tend to share a few traits:

• Discoverability is considered during product and content design, not after the fact

• Information is structured consistently across the organisation

• Entities, terminology, and positioning are stable, not improvised

• Measurement focuses on what’s knowable, not what’s fashionable

This isn’t “AI strategy.” It’s the work that should have been happening all along—and the organisations doing it don’t need a new acronym to justify it.

For a deeper critique of why most “AI strategies” collapse under scrutiny—and why this is fundamentally an organisational problem, not a tooling one—see Your AI Strategy Isn’t a Strategy.

A grounded outlook for 2026

What persists: Retrieval infrastructure. Structured content. Organisational discipline around discoverability. The boring, foundational work that compounds regardless of which interface sits on top of it.

What doesn’t: The chatbot on your pricing page that confidently invents discount codes. The “ask our AI” widget nobody asked for. The assumption that adding a conversational layer improves an experience that worked fine as a list.

2026 is when the AI graveyard starts filling up—quietly, without retrospectives, and with a lot of “we’re refocusing on core functionality” in the changelogs.

The conversation around AI and search will get less noisy and more honest. The industry will stop chasing interfaces and start respecting systems. Organic search won’t disappear—it becomes the stabilising layer that AI relies on.

The future of organic search in an AI-first world isn’t about learning how to talk to machines differently. It’s about making sure there’s something coherent, accurate, and worth retrieving when they do the talking.

This is an English adaptation of my interview with Roberto Serra, originally published in Italian on roberto-serra.com. Roberto runs SEO Confidential, a series featuring conversations with international SEO professionals. I’m sharing my responses here for English-speaking readers.

Roberto: If a growing part of SEO work will be absorbed by AI, what skills will really be crucial to remain useful to clients and not be replaced by automation?

Pedro: I believe that artificial intelligence will absorb the operational aspects of SEO rather than the strategic ones. Automation will handle tasks such as tool usage, data analysis, and pattern identification. As a result, I think SEOs need to prioritize critical thinking and strategy, demonstrating their value through innovation and creative problem solving. Ultimately, SEO professionals need to evolve into growth-oriented decision makers who oversee operational execution.

Roberto: You say that publishing too much know-how ends up feeding models that then compete with content creators. Given that many systems ignore blocks and continue to collect data, what strategy can a professional adopt today to protect their value without losing online visibility?

Pedro: I believe that if you publish everything you know for free, you will likely end up with a weak competitive advantage, if any at all. Artificial intelligence was created to meet specific problem-solving needs, and most of us use it to get things started quickly. However, as the capabilities of artificial intelligence increase, so do its skills. The more training data it consumes, the more capable it becomes.

SEOs have historically been big advocates of open knowledge sharing. But in an industry driven by strategy and the need to stay ahead of the curve, this openness could lead to a race to the bottom, as AI absorbs your best insights.

Now it’s up to each professional to assess how much they’re willing to invest in publishing and how in-depth their content needs to be. The goal is to find a balance: reveal enough to showcase your expertise, but hold back enough to protect your competitive advantage.

Roberto: Cloudflare offers sites much more explicit control over the use of content by AI. Could this be the beginning of a real redefinition of the pact between creators and platforms, or does it remain a symbolic gesture until Google, OpenAI, Anthropic, and other Big Tech companies clarify how they intend to move forward?

Pedro: We urgently need a control mechanism to safeguard our interests. Every content producer, creator, artist, and researcher should have a say in how and by whom their content is used.

The open web should not be a free archive where anyone can simply take and reuse content without agreement or consent. We experienced a similar change in the early 2000s when search engines emerged, and we should have learned from that experience.

While the implementation of the robots.txt exclusion protocol was a step in the right direction, it’s really like an open glass door with a guest list written on a Post-it note: anyone can walk in without asking permission or being on the list.

Regardless of the gestures of goodwill or data controls offered by tech companies, we need a system that gives unilateral and undisputed control to creators. Otherwise, I fear that the incentive to produce content will vanish, leaving us with a significantly poorer web.

Roberto: Cloudflare attributed a recent global blackout to a single faulty query that crashed the bot protection system. Is this an isolated incident, or does it reveal a structural fragility in the infrastructure that supports a huge part of the web?

Pedro: I believe that any single point of failure in such critical infrastructure should be cause for great concern. We have become heavily dependent on web services for both our work and our daily lives. Almost all appliances, electronic devices, and smart gadgets connect to a CDN or AWS instance somewhere.

As a result, it is unacceptable for the entire ecosystem to come to a halt because of a single point of failure. Just as we have multiple routes and modes of travel to choose from, we need to create digital redundancies to ensure that the web continues to function even if a single service provider fails.

Roberto: Many tools promise to measure visibility in AI responses, but there are huge doubts about personalization, context, and the technical limitations of the models. If these factors are not considered, what kind of reality do those numbers really represent? Why do you think part of the SEO community seems so reluctant to really question the quality and reliability of the data these tools provide?

Pedro: They represent a statistical hallucination. When tools ignore the non-deterministic nature of LLMs, coupled with a lack of access to user context, the numbers they produce are essentially “garbage.”

We are dealing with stochastic black boxes, not deterministic indices. Therefore, these metrics do not represent a “ranking,” but rather a “lottery ticket.” A tool might tell you that you are visible in a response, but without understanding the temperature, seed, and specific context of the user, that data is just noise masquerading as insight.

As for the lack of reaction from SEOs, I believe it is because the alternative is to admit that we are currently flying blind. The industry is drowning in a sea of illusions; agencies and professionals are under enormous pressure to prove the ROI of “AI strategies.”

Questioning the data would mean admitting that the “directional” charts they show clients are largely based on assumptions. It’s easier to accept the “mirage” of AI’s exclusive visibility than to admit that there is currently no scientific way to measure it, especially when even the engineers who build these models are unable to fully explain the inference path.

Roberto: Many of GEO’s “novelties” are nothing more than SEO principles that should have always applied, such as brand, authority, and product quality. How, then, do you explain that part of the industry continues to chase superficial metrics and sell old practices as if they were revolutionary? And what risks do those who build their strategy on this narrative run?

Pedro: It is a classic case of “magical thinking” fueled by the frantic need to remain relevant. By assigning acronyms such as GEO or AEO to fundamental principles such as “clarity” and “structure,” the industry creates an artificial battle to sell new services.

This suggests that LLMs have developed a sophisticated and unique taste for quality that Google has somehow overlooked, when in reality optimization is still only about how machines acquire and understand data.

The risk is that people are building their strategy on “temperature and noise” rather than sustainable foundations. If you think you’ve uncovered a secret “AI-only” variable because a chatbot turned your brand into a phrase, you’re confusing a probabilistic roll of the dice with a strategy.

The danger is that when you optimize for stochastic token prediction rather than deterministic foundation (RAG), the results are random. You haven’t outsmarted an algorithm, you’ve just gotten lucky in the chain of inferences. That’s not a business model, it’s gambling.

Roberto: When it comes to tracking visibility in AI responses, many compare prompt personalization to traditional search result personalization. How accurate is this equivalence, and what role do the now-obvious limitations of keyword tracking and query group-based systems play in this context?

Pedro: This is a fundamental misunderstanding of the difference between an index and a generator. Comparing the two is technically incorrect. Traditional search is deterministic retrieval; AI responses are probabilistic predictions.

The only predictable part of an AI response is “grounding” (RAG), which relies on the exact same retrieval mechanisms (indexing, vector search) as traditional search. There is no magical “AI retrieval layer” separate from technical reality.

The limitations confirm that most “AI tracking” is useless. Since we cannot see or extract the context-based personalization of an LLM, and since we have no verifiable data on how users actually query these systems, any tool that claims to track this aspect is “reaching assumptions.”

If you are not indexable and analyzable according to standard search principles, you cannot be used for grounding. Therefore, “new” visibility is just old retrieval visibility with a pair of fake whiskers. Until we have data that is not just “junk” noise, we will treat stochastic parrots as oracles.

A note for readers unfamiliar with the term: “stochastic parrot” describes an AI that repeats what it has learned from data without really understanding it. The model analyzes huge amounts of text, identifies the most likely patterns, and combines them to create new sentences. The result sounds sensible, but there is no understanding behind it—only statistics.

Roberto: The study by Kaiser and Schulze shows that traffic from ChatGPT is almost invisible and converts very little, while Google continues to dominate the decision-making phase. How do you assess this clear gap between “understanding” and “buying” in user behavior?

Pedro: This gap is not surprising: it is the architecture of the technology. Search engines function as transit hubs: their value lies in redirecting you to a destination. LLMs function as destinations: their value lies in synthesizing the answer so that you don’t have to leave.

The study confirms exactly what technical reality dictates: when an AI successfully uses grounding (RAG) to respond to a query, it satisfies the intent in situ. The “low traffic” statistic is actually proof that the models are doing their job: extracting information and eliminating the need to click.

As for the conversion gap: users are not stupid. When money is at stake, we want deterministic certainty. We trust a navigable list of verified websites/suppliers (Google) rather than a probabilistic recommendation from a chatbot that may be unreliable. We use AI to summarise the manual, but we use research to buy the car.

Roberto: EBU–BBC studies show that AI assistants are wrong almost half the time and base many answers on absent or misleading sources. How do you assess the impact of this unreliability on the visibility and reputation of companies that depend on online search?

Pedro: This study confirms what technical realists have been warning about from day one: the race for “AI visibility” is actually a gamble.

I recently shared a study highlighting how hallucinations are a structural feature of the design, not a bug (here is the link to the paper).

These models are not designed for truth, but for plausibility. By definition, they prioritise fluency over accuracy. Therefore, when the BBC detects high error rates, we are not necessarily witnessing a malfunction, but the system functioning exactly as designed.

If these models hallucinate in 50% of cases, fighting to be included in their responses means that statistically you are just as likely to be part of a lie as you are to be part of a fact.

For a brand, this is not just an accuracy issue, but a serious brand safety crisis waiting to happen. We need to stop pretending that LLMs are knowledge bases; they are probabilistic sentence completers. When a model invents a source or attributes a false claim to a company, it is not “making a mistake” in the human sense of the word, but simply predicting the next plausible token based on its training weights.

For companies, this creates a dangerous paradox: the industry pushes them to optimize a platform that could confidently destroy their reputation in the next inference.

Until Grounding (RAG) mechanisms are rigorously applied and the hallucination rate drops from “coin flip” to “near zero,” building a strategy around AI visibility is not marketing, but is highly likely to turn into liability management. This reinforces why deterministic and reliable search remains the only safe environment for transactional and factual queries.

Roberto: Academic research talks about “brain rot” in models trained with poor content, triggering a cycle of degradation that is difficult to reverse. What kind of risks do you see for the entire information ecosystem if models continue to feed on data generated by other AIs?

Pedro: I see this dynamic turning into a self-fulfilling prophecy, or what researchers call Model Collapse. A “digital ouroboros” is being built—the snake that devours its own tail. Artificial intelligence models are probabilistic engines that operate by converging toward the mean; they attenuate exceptions to generate “average” and plausible content.

Innovation, however, arises from exceptions. If the ecosystem is flooded with synthetic and uniform content, and models begin to train on that material, the quality of information slips into a cycle of error and mediocrity.

This process runs faster because of the economic factor mentioned earlier: incentive. If content creators are not rewarded for novelty because their work is extracted without consent or attribution, they will simply stop publishing.

AI cannot invent: it can only remix.

If you remove the human incentive to produce the “new,” you cut off the fuel that feeds these models. The likely result will be poorer AI and a stagnant culture.

Thanks to Roberto Serra for the thoughtful questions. You can find the original Italian version on his site.

Here’s the uncomfortable truth: most of what’s being sold as a revolutionary new discipline is foundational search engine optimisation, repackaged with worse data and better marketing.

But there’s a more interesting conversation buried under all this noise—one that decision-makers and practitioners alike keep missing. The real opportunity isn’t a new technical discipline. It’s an organisational one.

The Technical Reality: Grounding Is Just Retrieval

Let’s dispense with the mysticism.

When an AI system provides a factual answer and cites a source, it isn’t exercising judgment or developing preferences. It’s running a retrieval process—typically Retrieval-Augmented Generation (RAG)—to fetch relevant information before generating a response.

That retrieval step is, mechanically, a search task. It relies on indexing, vector search, and relevance scoring. The same principles we’ve been optimising for in traditional search for two decades.

If your content appears reliably in grounded AI responses, it’s because you’re crawlable, parsable, and topically relevant. If it doesn’t, no amount of “AI-specific optimisation” will save you. The grounding layer doesn’t run on different physics. It runs on the same information retrieval fundamentals; just with a generative interface on top.

The supposed evidence for “two different rulebooks”—a site appearing in ChatGPT but invisible in Google—isn’t evidence of a new form of authority. It’s evidence of noise.

Google operates on largely deterministic retrieval. LLMs operate on stochastic token prediction. When a chatbot surfaces your brand in a response despite your absence from traditional SERPs, you haven’t unlocked a secret AI ranking factor. You’ve won a probabilistic lottery. That’s not a strategy. That’s a slot machine.

The predictable part (and the part you can actually influence) is grounding. And grounding is retrieval. And retrieval is search. Boy! This sounds familiar…

The Measurement Problem: Your “AI Data” Is Guesswork

This is where the new tooling ecosystem falls apart under scrutiny.

A wave of SaaS products now promise “AI visibility tracking” and “insight into the black box.” What they’re actually offering is inference based on outputs you can’t verify, from systems you can’t interrogate, personalised by context you can’t see.

These models are non-deterministic. They don’t expose query data. They don’t provide reliable attribution. When someone claims their “AI rankings” are up while their search visibility is down, and calls the data “directional,” what they mean is: “This is fabricated, but I hope the noise confirms my priors.”

There is currently no scientific method for measuring the ROI of “optimising for AI” because the engineers building these systems cannot fully explain the inference process themselves. If the scientists can’t map the output path, a dashboard with a confidence score certainly can’t either.

This doesn’t mean you should ignore AI as a discovery surface. It means you should be honest about what you can and cannot measure, and build strategy on the parts that are actually knowable.

The Real Opportunity: Discoverability as Organisational Capability

Here’s where the conversation gets more interesting.

The frantic search for “AI optimisation tactics” misses a larger point: discoverability—whether by traditional search engines, LLMs, or any future retrieval system—is not a channel to be gamed. It’s a capability to be built.

And that capability doesn’t live in the SEO team. It lives in how an organisation structures, surfaces, and communicates its information across every function.

The companies that will actually benefit from AI-driven discovery aren’t the ones hiring “AEO specialists” or buying new AI prompt tracking subscriptions. They’re the ones treating discoverability as a product concern, a content architecture concern, and a communications concern. Not a marketing afterthought bolted on post-launch.

This is the real shift. Not a new acronym. An organisational reframe.

When discoverability is embedded into how a business operates—into product development, into content strategy, into how teams think about information architecture—you stop optimising for individual channels and start building something that works across all of them.

Traditional SEO. AI grounding. Voice interfaces. Whatever retrieval surface comes next. The fundamentals are the same: be crawlable, be parsable, be relevant, be complete.

The question isn’t “how do we optimise for ChatGPT?” It’s “how do we build an organisation where discoverability is a first-class concern?”

Why This Keeps Not Happening

If this reframe is so obvious, why isn’t everyone doing it?

Because the incentive structures of the industry actively work against it.

Commercial pressure favours activity over outcome. Agencies and consultancies often need to demonstrate constant, visible work to justify retainers. It’s far easier to present a report on a new tactic or a reaction to a named algorithm update than to explain the slow, often invisible progress of foundational improvements. Selling “quick wins” is an easier business model than selling architectural discipline, even when the latter delivers more value.

The attention economy rewards novelty over rigour. Personal brands in the SEO space are built on being first to a new technique or trend. “Let’s wait for the data” doesn’t generate engagement. “New ranking factor discovered!” does. This creates a cycle where practitioners feel pressure to adopt new terminology and tactics to appear current, even when critical evaluation would suggest caution.

Tool marketing shapes workflow. Software vendors market features, and practitioners often adopt tool-centric thinking—solving for the tool’s checklist rather than the actual retrieval problem. When your workflow is organised around what a dashboard can measure, you optimise for the dashboard, not for discoverability.

Fear and insecurity drive short-termism. Imposter syndrome is rampant in the industry. Many practitioners, uncertain about their depth of knowledge, believe they must constantly chase the newest thing to avoid appearing outdated. Advocating for “boring” fundamentals feels professionally risky, even when it’s strategically correct.

KPIs are misaligned. When success is measured by narrow metrics, like rank for a vanity keyword, visibility in a single tool; rather than business outcomes, the focus naturally shifts to short-term tactics. The architectural work that delivers compounding returns gets deprioritised because it doesn’t move the number this quarter.

Recognising these forces isn’t defeatism. It’s the first step to building something different.

What This Actually Looks Like in Practice

Treating discoverability as organisational capability sounds good in a strategy deck. Here’s what it means in operational terms.

Discoverability in Product Development

If the first time someone thinks about search is after a feature launches, you’ve already lost months.

Discoverability considerations belong in product requirements. Not as an afterthought, but as a core question: How will users find this?

This means asking, at the requirements stage:

• What information need does this address, and how are people currently expressing that need in search?

• What’s the URL and information architecture strategy for this feature or content?

• How does this fit into the existing site structure, and what internal linking is required?

• What structured data is needed to make this parsable by machines (search engines and LLMs alike)?

• What are the dependencies on engineering, content, and design to make this discoverable at launch rather than six months later?

When these questions are part of the product process, you stop playing catch-up. SEO becomes a constraint that shapes better products, not a cleanup crew called in after the damage is done.

This requires SEO (or whoever owns discoverability) to have input at the roadmap and requirements stage. If they’re only consulted post-launch, the org structure is the problem.

Content Architecture as Technical Infrastructure

Most organisations treat content as a marketing asset: something the content team produces, the SEO team “optimises,” and the CMS stores.

This framing is inadequate for a world where machines need to retrieve, parse, and synthesise your information across multiple surfaces.

Content architecture — how information is structured, related, and surfaced — is technical infrastructure. It should be treated with the same rigour as your database schema or API design.

In practice, this means:

• Entity modelling. Understanding what concepts your organisation needs to be known for, and ensuring those entities are consistently represented across your content. Not keyword lists — actual semantic entities with defined relationships.

• Schema implementation. Structured data isn’t a “nice to have.” It’s how you communicate unambiguously with machines. This should be systematic across the site, maintained as code, and validated in CI/CD pipelines — not manually added to individual pages by the SEO team.

• Internal linking logic. Links aren’t just navigation. They’re signals of relationship and importance. Internal linking should follow a defined architecture, not the whims of whoever last touched a page.

• Content inventory discipline. Knowing what content exists, what state it’s in, what entities it covers, and how it relates to other content. Most organisations cannot answer these questions. Without answers, “content strategy” is guesswork.

When content architecture is treated as infrastructure, it becomes maintainable, auditable, and scalable. When it’s treated as a marketing deliverable, it accrues debt until someone declares SEO bankruptcy and starts again.

Distributed Ownership, Centralised Enablement

“Who owns SEO?” is usually the wrong question. If the answer is “the SEO team,” you’ve created a bottleneck and an excuse.

Discoverability is everyone’s concern: product, engineering, content, design, marketing. The SEO function shouldn’t be doing all the work. It should be enabling others to do the work correctly and auditing the outcomes.

A more useful ownership model:

• Product teams are responsible for discoverability of their features. This is part of their success criteria.

• Content teams are responsible for content architecture and entity coverage within their domains.

• Engineering is responsible for technical foundations: crawlability, rendering, structured data implementation, performance.

• The SEO/discoverability function provides standards, tooling, training, and audit. They’re the centre of excellence, not the centre of execution.

This doesn’t mean less SEO expertise. It means SEO expertise is leveraged across the organisation rather than siloed in a team that’s always behind.

The shift is from “SEO team does SEO” to “SEO team makes everyone better at discoverability.”

Measurement That Acknowledges Reality

Here’s what you can measure with reasonable confidence:

• Organic traffic and its contribution to business outcomes (conversions, revenue, qualified leads)

• Crawl behaviour and indexing status (this KB article on Log-file analysis might help)

• Ranking positions for defined keyword sets

• Technical health metrics (Core Web Vitals, crawl errors, structured data validation)

• Content coverage against your entity model

Here’s what you currently cannot measure reliably:

• “AI visibility” or “AI share of voice” (this KB article on Visibility in LLMs has more detail)

• Attribution from LLM-driven discovery

• The impact of specific “AI optimisation” tactics

Stop pretending bad data is “directional.” It isn’t. It’s noise dressed up as signal.

When stakeholders ask about AI performance, the honest answer is: “We cannot reliably measure this yet because the systems don’t provide attribution data and the outputs are non-deterministic. What we can tell you is that the fundamentals that drive AI grounding are the same fundamentals that drive traditional search, and here’s how we’re performing on those.”

This is harder to sell than a dashboard with an “AI Score.” It’s also true.

Measurement discipline also means focusing on leading indicators over vanity metrics. Rank for a single keyword is a vanity metric. Indexed coverage of your entity model is a leading indicator. Traffic to a page is a vanity metric. Conversion rate from organic entry points is a business metric.

When measurement is honest, strategy becomes clearer. When measurement is theatre, strategy is just storytelling.

The Actual Work

None of this is new. That’s rather the point.

The organisations that will benefit from AI-driven discovery, and from whatever retrieval surfaces come next, aren’t the ones chasing acronyms. They’re the ones doing the foundational work: building crawlable, parsable, semantically rich information architectures and embedding discoverability into how the business operates.

That work is slow. It’s often invisible. It doesn’t produce exciting conference talks or LinkedIn posts about “cracking the AI algorithm.”

It does produce compounding returns across every machine interface that needs to understand what you do and why you matter.

The practitioners who will thrive aren’t the ones mastering “GEO” or “AEO.” They’re the ones who understand information retrieval deeply enough to recognise that the fundamentals haven’t changed—and who have the organisational credibility to embed those fundamentals into product development, content architecture, and business strategy.

The strategic imperative isn’t “optimise for AI.” It’s “build an organisation where discoverability is a first-class concern.”

That’s not a new strategy. It’s the strategy we should have been running all along.

The professional evolution of a consultant, especially those in SEO, isn’t always smooth. Without going into the merits of what counts as evolution, the SEO role it’s a sinuous path and a complex and iterative process. I’m a firm believer that, the best professionals arise from being constantly challenged on the most diverse situations and problems. The more diverse issues you solve, the more competent you become at your job! This process, mostly involves improving a logical and self-critical way of thinking in problem solving; something so necessary these days. Those who do not correct the route and try to go the easy way—which does not foster this way of thinking—likely end up becoming just yet another “expert”.

Aligning expectations in SEO

The alignment of expectations, in general, depends not on two, but on three interlocutors: the medium, the customer and the supplier. The medium is usually the setter; the party dictating the rules. The client and the supplier try to organize themselves, and make their goals work on the rules laid down by the medium. Nevertheless, the medium can either be controlled by one of the other interlocutors, or completely independent.

Whenever there is no alignment of expectations, we decrease the likelihood of achieving goals. For both the customer and the supplier end up with an individual perception of what they know about the medium—or think they know—instead of staying focused on understanding and studying the facts. This is what often leads us to try to solve the wrong problems, and what we usually call “ass-u-me”.

Misalignment usually arises from lack of understanding of the medium (or problem) and its components. Either by one or both parties. And this is the phenomenon we see both in customers, who think they know what they are asking, and suppliers, who think they know what they are doing. In the end, what area would be better for all this to conflate than SEO?

Misalignment of expectations in SEO

Believe it or not, but there are entire businesses, relying solely on top of SEO. The belief that, SEO is the area capable of unilaterally creating, or even saving an entire business. Businesses where a competitive advantage or a marketing strategy has never been defined. Businesses that believe that SEO is “the solution” that will solve everything that was wrong or did not work. How, in the age of Artificial Intelligence, are there beliefs that a business will succeed without a competitive advantage, or a defined market position, is something that goes beyond my ability to understand.

Unfortunately, misalignment of expectations are more frequent than we would wish. In SEO, this usually happens because:

1. There is a misconception of how a medium (search engine) works—limited understanding of the medium;

2. There is a misconception of what SEO can do for a website—between customer (potential benefit of the service purchased) and supplier (potential benefit of the service sold);

Bottom line: None of us has a clue on how search engines work, and no one really understands what they are buying and selling with respect to SEO. It’s all on the “ass-u-mptions”.

My point here is not to judge what you or I know about SEO or search engines. But to shed some light on the critical need to align expectations. If you’re already thinking about jumping in the comments with pitchforks, you got the wrong message.

But let’s face it, quality of services aside, all of this exists because there is a market. Because there are uninformed people, who prefer to let themselves be deceived by pompous adjectives, than to try to understand what they’re buying. The customer who buys the link building package; the shallow content marketing; the focus on keyword density, or the pursuit of myths. They do it because they think it’s something that holds value. Not because they made an informed decision, or tried to get opinions about what they’re buying. Most of the time, the business only moves forward by confirmation bias: My assumption tends to empathise with another person’s assumption when it aligns. Expectations!

SEO fixes everything… Or maybe not

SEO does not create value where value does not exist. SEO exposes value! And for something to succeed, value must exist. I speak about this in practically all my lectures, mentoring, and alignment with clients. And I think it’s something that should be very clear, even before selling any service. In layman’s terms: If my website/product is crap, and I optimize my website, I’ll just expose the crap that is my product. I will not necessarily turn crap into strawberries.

Those selling optimisation, need to sit longer with their customers and make that alignment of expectations. Understand what the customer’s goal is, and what an optimisation service can do for their site. This implies the consultant to have a minimal strategic notion of what to do. A mere tool operator approach will likely fail spectacularly.

For example, in SEO what’s the main approach that folks tend to see as solution even without having understood the client’s product right?—Link Building! We immediately think of how to gain links, without even having designed a coherent strategy. This is a reflection of a misaligned approach to a basic concept of value proposition.

It’s urgent and imperative to stop seeing SEO as what you do after having already tried everything else that didn’t work. Or that ranking better in search results is the sole strategy that proves that your product is better than your competitor’s. SEO needs to be seen as a continuous, iterative, and cumulative process. It’s like exercising: when you stop, you get fat!

SEO omnipresent

Sorry, couldn’t help it!–Joking aside, in the past I have already talked about the need for a holistic approach in SEO. This need has not changed. On the contrary, it has increased.

SEO is a component of Digital Marketing, which permeates not only departments, but also moments. That is, the right moment to think about SEO, is at all times. But while the goal is something more enduring, strategy and tactical approaches may vary depending on the timing of a business’s growth. If we take a standard approach, blindly following what tools say and leaving room for misalignment, we run the risk of doing the wrong things at the wrong times. And if there is anything worse than building a weak house, it’s wanting to start the house from the roof.

If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions. — Albert Einstein

I believe in the constant dedication of time and knowledge needed to understand the problem I am facing. Going for a timely, tool-based and simplistic approach is not synonymous with “focus on SEO”, but rather synonymous with a limited approach and that strategically adds little value.

But that doesn’t mean you can ignore all of them. The question isn’t whether 404s hurt your site. The question is which ones matter and why.

Do 404 Errors Hurt My Site?

No. Not directly. But before you close this tab feeling vindicated, let me explain why you probably still need to pay attention to some of them.

404 errors don’t tank your rankings. Google has said this repeatedly. I’ve said this repeatedly. And yet, every few months, someone panics about their Search Console report like it’s a cancer diagnosis.

Here’s the thing: HTTP 404 is a status code. It means “I looked for this thing and it doesn’t exist.” That’s it. It’s not a penalty. It’s not a red flag. It’s just... information.

The real question isn’t “are 404s bad?” It’s “why is this 404 happening, and should I care?”

External 404s: Someone Else’s Problem (Mostly)

When someone links to a page on your site that doesn’t exist, that’s an external 404. Could be a typo. Could be they linked to something you removed. Could be someone hacked your site six months ago and injected URLs that you’ve since cleaned up.

The question you need to ask: Should this URL exist?

If yes—redirect it. If no—ignore it and move on with your life.

Google doesn’t know which URLs matter to you. It reports everything it finds. That’s actually useful, but it doesn’t mean every reported error needs action.

Internal 404s: Now We’re Talking

This is where it gets more interesting. Internal broken links are links on your site pointing to URLs on your site that don’t work.

If you’re linking to pages that don’t exist, you’re wasting crawl budget, creating dead ends for users, and generally making your site feel like a building with hallways that lead to brick walls.

Search Console won’t tell you about these. You need to crawl your own site. Screaming Frog for smaller sites (under ~20k URLs), Sitebulb or OnCrawl for bigger ones.

Fix these. Not because Google will punish you, but because it’s sloppy and users notice.

Internal Origin, Internal Destination: Clean Your House

When both the link and the broken destination are internal, you have full control. There’s no excuse for letting these accumulate.

A site with a high percentage of internal broken links is a site that hasn’t been maintained properly. Unless you’re a government website, in which case broken links are basically a feature.*

*Every time I work with a government site (Brazilian or Portuguese, take your pick), I’m amazed at how much information just... vanishes. Entire systems go offline without explanation. If you bookmark government pages, good luck.

What About Soft 404s?

A soft 404 is when your server returns a 200 OK status (or a redirect) for a page that should return 404. Classic examples: redirecting deleted product pages to the homepage, or showing a “page not found” message while the server claims everything is fine.

Google hates these. Why? Because you’re lying to it. You’re saying “this page exists and is fine” when it doesn’t and isn’t.

If a page doesn’t exist, return a proper 404 (or 410 if it’s permanently gone). Don’t try to be clever with redirects unless you’re actually redirecting to equivalent content.

The Redirect Trap

Here’s where most people get it wrong: they think every 404 needs a redirect.

Wrong!

Redirecting a 404 makes sense when:

• The content moved to a new URL

• There’s a logical replacement page with similar content

Redirecting a 404 is a bad idea when:

• The URL never should have existed

• It came from spam or hacked content

• You’re just trying to make the error go away

Redirecting garbage URLs to your homepage is how you turn legitimate redirects into soft 404s at scale. Google will eventually start ignoring your redirects altogether if enough of them are bogus.

Sitemaps: Keep Them Clean

If you have 404s in your XML sitemap, you’re telling Google “please crawl these URLs that don’t exist.” Every time. Forever.

During a migration, having some temporary noise in your sitemap is fine. Leaving it there for months? That’s how you train Google to stop trusting your sitemap.

The robots.txt Mistake

Some people try to block 404 URLs in robots.txt. This is backwards.

If Google can’t crawl the URL, it can’t see the 404 status. Which means it might keep the URL indexed, thinking it’s being blocked for a reason. Let Google see the 404. That’s how it learns the page is gone.

So What Should You Actually Do?

1. Find the 404s that matter. Use a crawler on your own site. Internal broken links are your responsibility.

2. Check Search Console, but don’t panic. External 404s are often noise. Look for patterns—if important pages are being linked to incorrectly, fix them. If it’s random garbage, ignore it.

3. Redirect when it makes sense. Old URL moved? Redirect. Random spam URL? Let it 404.

4. Return proper status codes. 404 for pages that are gone. 410 if they’re gone forever and you want to be explicit about it. Don’t fake 200s.

5. Keep your sitemap clean. Only canonical, indexable URLs. If it’s not something you want indexed, it shouldn’t be in there.

404s aren’t something Google punishes you for. They’re a normal part of how the web works. Content comes and goes. Links break. That’s life.

What matters is understanding why they’re happening and whether you should do something about it. Usually, the answer is “fix the ones you control, ignore the ones you don’t.”

And if you’re spending hours chasing every 404 in Search Console, you’re probably optimising the wrong thing.

At Google, the first steps with machine learning began at least when Google started fighting spam in 2002. We saw the first, more emphatic signs of something more evolved when Google released its behavioural and quality analysis algorithm, Panda. But the use of technologies that reduce dependence on factors that are easy to manipulate has practically always been an ambition of all search engines.

What is RankBrain?

RankBrain is a form of artificial intelligence that Google is using to serve better search results and answers to its users. The need arose due to the introduction of different search habits and methods—voice search, for instance, makes many queries more difficult for a machine to interpret.

In 2007, Google had already mentioned the diversity and complexity involved in interpreting user searches. Udi Manber revealed at the time that between 20-25% of daily searches were entirely new. Currently, according to public statements, that number is somewhat lower (around 15%). However, considering that mobile search volume has increased considerably—even surpassing desktop search—we need to consider what might have contributed to this percentage decrease. My bet is on two strong characteristics of mobile search:

• A higher volume of shorter searches — when on a mobile device, people don’t type as many words, resulting in more head-term queries

• Voice search — which massively diversifies a smaller quantity of searches, though it’s a growing trend

We’re also starting to notice another habit that will influence these statistics: searches made through automatic features like contextual assistance on mobile devices.

What is Artificial Intelligence and Machine Learning?

This is where almost everyone who wrote about the subject gets lost. I’ve read things like “[…] machine learning is a type of artificial intelligence […]” — Sorry, what? Artificial intelligence is a scientific field and machine learning is a methodology. The scientific field uses the methodology to evolve. That is, AI uses ML as one of the means to learn and evolve.

As I mentioned above, in Google’s case, machine learning methodologies have been used (at least) since 2002 for different purposes. Currently, with RankBrain, it’s being used more extensively to learn to make connections between word vectors.

The complexity of the vocabulary we use to express ourselves is immense. In some languages more than others, but generally, interpreting search intent is far more difficult than interpreting words. Word vectors are important for machines to interpret, with a smaller margin of error, the different intentions and meanings that derive from different word associations. Consider that an association between two or more words can have countless meanings… And we’re not even considering more complicated things, like intonation.

For those who remember Hummingbird (an infrastructure improvement Google made in 2013), it served as the foundation that allowed Google to bring RankBrain to us. Just as Caffeine in 2010 enabled Google to achieve improvements in indexing speed.

Has RankBrain already launched? Has it reached us yet?

RankBrain was launched before it was announced. Google disclosed that it was gradually rolled out in early 2015. Currently, all Google improvements are launched internationally. The habit of thinking “this will only reach [my country] in X years” is a distorted perception of reality.

How do I do SEO for RankBrain?

If you understood everything I’ve written so far, you’ll arrive at the simple conclusion without much effort: it doesn’t make sense.

Google mentioned that RankBrain is among the most important factors for ranking results. However, this importance appears precisely because it’s an algorithmic improvement that’s practically impossible to manipulate. This is because RankBrain acts as a modifier, not as a base algorithm. There isn’t even a predefined condition for when it should act. Modifier algorithms come in at the final phase, before serving results to users. And they’re not static. They adapt to the type of search made, as well as to the multiple implicit or external factors that can influence a search.

So, if you’ve read or heard someone explain how to “optimise for RankBrain,” congratulations—you’ve identified a charlatan.

Consider that nowadays, search methods and habits have changed. Google is no longer just a search box that returns ten blue links. Just as search habits have evolved, search engines try to keep pace and force professionals to evolve… Well, at least some of them.

As mentioned above, RankBrain emerged due to the need to accommodate new search habits and improve search results. The devices largely responsible for changing users’ search habits are mobile devices and the omnipresence of the internet in people’s daily lives.

Previously, the information sources that search engines had about their users only allowed them to understand isolated data points—like search history and unreliable IP addresses. Today, the amount of information implicit in a search using a smartphone is immense. It’s no coincidence that search engines say mobile is made of moments. The moment you’re waiting on the street and search for something is different from the moment you’re in front of the TV and search for the same thing. Which is also different from the moment you’re in the bathroom and search. Today, devices can digitise and provide implicit data like precise geolocation and location history, time of day, speed, temperature, search habits, among others.

How does RankBrain impact the future of search?

If you’ve attended one of my talks, or discussed anything related to the future of Google and search with me, you’ve probably heard me cite Google’s mission:

Organise the world’s information and make it universally accessible and useful.

Note that the phrase mentions “the world’s information” and not “the internet’s information.” Google aims to bring all information, offline and online, to your fingertips. To be your personal assistant.

Users are predisposed to provide some type of information. However, both the quantity and sensitivity of that information requires that something advantageous and convenient be offered in return. The value of the information we’re willing to give up varies proportionally with the perceived value or convenience of the service provided in exchange.

If Google can be more intelligent and make our lives easier in every moment of the day, we’ll be far more predisposed to share details of our preferences so that information can be organised—maximising our wellbeing.

Will the world belong to robots? Skynet? Apocalypse?

Currently, everything is still very much in a “let’s see” phase. Especially everything that’s made publicly available. It’s indisputable that all online services experimenting with chatbots, virtual reality, or augmented reality are on the same path or have similar or complementary ambitions. Google has simply been on this path for much longer and, as a result, has technology that’s far more developed and advanced than much of the competition.

I imagine that in the very near future you’ll be connected 100% of your time to an omnipresent personal assistant. Whether you’re walking down the street, travelling in your car, or just opening your fridge door. This personal assistant will recognise all the different moments, as well as all your needs, and try to meet them in the best way possible. For anyone who wants a basic idea of what I’m referring to, I recommend watching the film Her (2013).

Ultimately, among Google’s main objectives are three fundamental ones: inform, disintermediate, and be your personal assistant.

But before we move ahead, it’s important to understand what really drives someone to suggest to work with SEO as pay-for-performance during a negotiation; there are several reasons. Among the most common are:

• The client wants to be sure it will pay for results;

• The client doesn’t have an allocated budget for SEO;

• The client has a partial understanding of everything that is involved in SEO;

The client wants to be sure it will pay for results

Probably because they had bad experiences in the past. From dealing with SEO professionals, who sold their services with great fanfare, but in the end failed both in expectation alignment and the delivery of positive results.

The client doesn’t have an allocated budget for SEO

In pay-per-click it’s relatively easy to transform investment into a result. You invest X on one side and expect X+Y to come out on the other. Unfortunately, in SEO the scene changes slightly as the number of factors involved makes it more difficult to calculate a reliable prediction, or quantifying achievements and results. While this perception does not change, it will be difficult for SEO to get the same level of investment of a pay-per-click campaign in mostly any search engine.

The client has a partial understanding of everything that’s involved in SEO

This is usually by far the most common reason. The client is not aware of all the areas and business departments involved and influencing a good optimisation work. This “limited understanding” can arise for various reasons, however, by far the most common is linked to bad experiences or low value of deliverables with previous consultants. After all, many of the big digital marketing agencies continue selling SEO purely as keyword research, titles, meta descriptions and meta tags; or you know, just “link build it”. Transmitting a notion that SEO is a one-sided job and the need for a client-side effort will be mostly non-existent. Things will happen as if by magic, and in two or three weeks, the site will already be enjoying the best rankings in search engines! — Think again!

Why don’t I usually work SEO as pay-for-performance?

SEO work in the performance model is not as simple as it might seem.
While in pay-per-click that might work because, in a way, you have direct control over what is displayed in Google’s sponsored search results and, at the same time, you don’t depend so much on IT to make necessary technical changes to increase your chances to achieve said results. In SEO, the dependence on third parties is much bigger and almost always inevitable.

Consider the following examples, and think about how this may influence the perception of quality of work for each side:

• If the server where the site is hosted stops responding, or even if there’s a problem in the CDN? Does this disqualify my work?

• If the domain suffers some kind of problem with the DNS? Does this disqualify my work?

• If the recommendations are implemented at a slower than desirable rate? Does this disqualify my work?

• If the recommendations are not 100% implemented? Does this disqualify my work?

• If an error occurs after the implementation and homologation? Does this disqualify my work?

• If the team Web Design or UX teams make changes without communicating, and they have a negative impact? Does this disqualify my work?

• If the Web Analytics team implements a slow or conflicting tag? Does this disqualify my work?

• If the sales team sets a product in the wrong way? Does this disqualify my work?

• If Google changes the search results interface? Does this disqualify my work?

• If the website has a history of bad practices and that hinders the recovery and overall performance? Does this disqualify my work?

• If the website is hacked, infected with malware, or starts distributing phishing attacks? Does this disqualify my work?

I think you got the point. These are just a few, among a huge number of factors I like to raise whenever the topic “SEO as pay-for-performance” arises.

In general, working in an SEO as pay-for-performance model creates — especially on the client side, and even if unintentionally — a lack of commitment, or lack of interest, in making things work within the defined terms, because if that happens it usually means the SEO consultant will get paid fat bonus. But this is only one side of the coin, and perhaps even the smallest of problems. Many clients also ignore the fact that there are also plenty of pseudo-professionals ready and willing to say “yes” to everything just to close the deal, but in the end will aggressively pursue all means just to show results… Any results! Even if, in the near future, that kind of aggressive work results in a manual or algorithmic penalty on your site.

Just as there are ways to manipulate search results, there are quality guidelines that must be followed in order to avoid troubles and serious problems. However, not all professionals can recognise the limits on how far they can push. A professional (or even agency) working under a “give me performance, show me results” whip will not worry too much with what can happen to your site in the search results after the end of the contract, especially when this means they won’t get the bonus.

The SEO industry still has a questionable reputation — Yes, it was worse a few years ago.

For a long time, a lot of professionals resorted to manipulative tactics to sell a service that was seen as “technical tricks and gimmicks”. Nowadays, if you want to achieve good and lasting results, one must include a strategic side, a deep knowledge of marketing, usability and technology; a holistic approach. SEO is no longer what we knew back in 2005. SEO in 2016 is, above all, digital presence.

I still want to work SEO as pay-for-performance

Right! It is possible that in some very specific cases it makes sense to work that way. But this is something that each of the parts (client and agency) must set according to the established goals. What I recommend here on the blog, and I describe above, is intended to only serve as a brainstorming exercise on what’s done or recommended in Digital Marketing in general and SEO in particular.

Remember that a working with SEO as pay-for-performance usually involves three essential things:

1. A solid alignment of expectations and scope of work;

2. A contract stating what will not be assessed, what will be evaluated and how;

3. Good measurement tools (preferably always more than one), which will assess results and market potential — It is good to remember that a good performance work implies having a vision of the potential, not just the result.