The Agent Runtime Wars Have Begun. Is Your Website Ready? via @sejournal, @slobodanmanic

The agent runtime is the new browser layer, and your website is going to be evaluated against the runtime, not against any individual model.

That’s a shift web professionals have not yet made. The conversation is still framed around models. Which model writes better? Which one cites more accurately? Which one’s API is cheaper this month? The model conversation is loud because new models ship every few weeks, and every release is theatrical.

The interesting story is the one underneath it. The foundation is being rebuilt. This week made it impossible to ignore.

The Runtime Stack Shipped In April

On April 15, Cloudflare shipped Project Think, a new Agents SDK built around durable execution with crash recovery and checkpointing, sub-agents that run as isolated children, persistent sessions with tree-structured messages, and sandboxed code execution running on Dynamic Workers. Within hours of the same day, OpenAI shipped the next evolution of its Agents SDK with native sandbox execution and a model-native harness. Two of the largest infrastructure operators on the web shipped competing answers to the same question, and the question was: how does a long-running AI agent actually run in production?

Then, on April 16, Cloudflare added five more pieces. AI Platform: a vendor-agnostic inference layer that routes models for agents. AI Search: a vector index plus chunking pipeline shipped as a managed product specifically for agent retrieval, competing with Pinecone and Algolia in the agent-side RAG layer rather than with Google AI Mode. Email Service in public beta, designed so agents can use the most universal interface in the world as a channel. PlanetScale Postgres and MySQL inside Workers. And the engineering foundation for hosting very large open-source LLMs like Kimi K2.5 directly on Cloudflare’s network.

Sundar Pichai described the same shift a week earlier. On the April 7 Cheeky Pint podcast with Stripe co-founder John Collison, he called Search itself an “agent manager”: “A lot of what are just information-seeking queries will be agentic in Search. You’ll be completing tasks. You’ll have many threads running.” Many threads per query is a runtime description of Search. Google’s CEO is pointing at the same substrate Cloudflare and OpenAI shipped this week.

If OpenClaw was the agentic web for consumers (a playable demo, an interesting prototype, something to gesture at), this is the agentic web for adults. Durable. Sandboxed. Auditable. The kind of infrastructure you would actually run a business on.

The pattern across all of it is one thing: the runtime. Not the model. Not the consumer chat app. Not the keynote slide. The runtime is the layer where agents are spun up, persisted across hours and days, given filesystem access, given network access, given memory. The runtime is the layer that decides whether an agent’s session survives a crash, whether its sub-agents can be reasoned about, whether its code execution is contained.

The Wrong Question And The New One

Web professionals have spent the last 18 months asking the wrong question. The question was: Which AI model should we optimize for? ChatGPT or Claude or Gemini or Perplexity. Whose citations matter more? Whose crawler should we let through? That conversation made sense when the models read your website directly.

They don’t anymore. The model reads what the runtime hands it. The runtime fetched your page. The runtime parsed it. The runtime executed (or did not execute) your JavaScript. The runtime resolved your structured data. The runtime negotiated authentication. By the time the model sees anything from your website, it is seeing the runtime’s interpretation of it.

The new question, if you take this week seriously, is which agent runtime your website is legible to. Three things to test before next week:

1. Do your most important endpoints return machine-readable structured responses, or do they only render correctly inside a full browser session?
2. Is your authentication scoped so an agent acting on a user’s behalf can hold a session across multiple calls, or does it only support one-shot human logins?
3. Does your structured data still mean the same thing if a runtime that did not execute your JavaScript tried to read it?

These are runtime-readability questions. The model has nothing to do with them. The runtime decides whether your answer is even in the model’s context window, and the model picks from whatever the runtime hands over.

The web’s plumbing is being rebuilt. Every model in the next two years will see your website through one of these runtimes, not directly. Your website’s job, starting now, is to be legible to the runtime.

The model conversation will keep happening on conference stages and in keynote slides. The runtime conversation is happening in product changelogs from infrastructure companies. The companies that ship the runtime will decide which websites get reached by AI search and AI commerce. Stop asking which model. Start asking which runtime.

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This post was originally published on No Hacks.

Featured Image: Viktoriia_M/Shutterstock

AI Companies Are Selling Heartwarming Ads – They’re Racing To Automate Your Job via @sejournal, @gregjarboe

OpenAI wants you to know that its technology helps you figure out what to cook for dinner. Google wants you to feel the warmth of a family settling into a new home with Gemini by their side. Anthropic would like you to see its Claude as the clean, trustworthy alternative to the ad-cluttered mess everyone else is building.

These are real campaigns, and they represent a deliberate strategic choice: Make AI feel human, domestic, and useful before anyone starts asking harder questions.

The harder question, for digital marketing professionals, SEO specialists, content creators, and entrepreneurs, is this: What are these companies actually building while they’re running heartwarming commercials?

What The Ads Are Saying

OpenAI’s consumer messaging has settled into a register of casual everyday utility. The “Dish” and “Pull Up” ads show ordinary people getting help with dinner or fitness routines, not productivity gains or enterprise automation. Google’s Gemini advertising has leaned into family milestones and emotional resonance, positioning the model as a companion for life’s significant moments. Anthropic, meanwhile, has run campaigns that explicitly mock sponsored responses in competitor products, casting Claude as the principled choice for users who don’t want their AI assistant quietly selling them something.

Each narrative is coherent, well-produced, and aimed squarely at building consumer trust. That trust, of course, is the infrastructure on which the enterprise business gets built.

What The Products Are Actually Doing

Behind the domestic warmth, all three companies are racing to deploy agentic systems capable of automating complex, multi-step professional workflows. However, this means marketing professionals will no longer be defined by their ability to perform individual tasks but by their capacity to design and manage autonomous systems that handle those tasks with minimal human supervision.

That’s a significant reframe. GPT-5.5 is being positioned as a project manager that can build entire lead funnels, including strategy, copy, and email deployment, without reprompting. Gemini 3.1 Pro’s one-million-token context window is designed for deep research at a scale that, as the roadmap puts it, “humans cannot replicate.” Claude Opus 4.7 is being marketed to enterprise clients for legal redlining, production-grade code review, and high-fidelity visual verification – work that currently employs specialists.

OpenAI has published a benchmark called GDPval that measures model performance across 44 occupations, from real estate broker to news analyst. Its latest model, GPT-5.5, scores 84.9%, a win-or-tie rate against human professionals on tested tasks. That’s not a consumer product metric. That’s a displacement metric dressed up in benchmark language.

Why This Is An SEO-Specific Problem

The traditional SEO model – research keywords, produce content, earn rankings, and drive clicks – is being restructured by the same companies that are running those warm family ads. Google’s AI Overviews, which Sundar Pichai confirmed are driving Search revenue growth of 19% in Q1 2026, are changing the click economy in ways the advertising doesn’t acknowledge. Users are getting answers without visiting pages. Brands are competing not for rankings but for citations within AI-generated summaries, a discipline some practitioners are now calling generative engine optimization (GEO).

The implication for content marketers is that volume strategies built on human-speed production are losing their edge precisely as AI tools make high-volume production cheaper and faster for everyone. The competitive advantage is shifting toward authority, entity recognition, and the kind of structural content quality that AI systems can parse and attribute. The people who figured out technical SEO before their competitors did will recognize this dynamic.

The Tension Worth Watching

There is a genuine contradiction at the center of all three companies’ public positioning. They are simultaneously telling consumers that AI is a helpful companion and telling investors that AI is automating professional-grade cognitive work at scale. Both things are true, and the gap between those narratives is where marketing professionals need to be paying attention.

Anthropic’s own researchers published findings showing that junior engineers who relied heavily on AI coding agents not only failed to complete tasks significantly faster – they also demonstrated weaker understanding of their work when tested afterward. If that extends to content strategy and SEO analysis, the profession faces a skills erosion problem that no “AI as partner” messaging addresses.

The companies building these tools have financial incentives to keep the consumer narrative warm and the enterprise narrative bullish. Your incentive is different: Measure what is actually happening to your traffic, your conversion rates, your citation share of voice, and your team’s capability development, and make decisions based on that data rather than the ads.

The dream they’re selling is appealing. Ground truth it anyway.

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Featured Image: Frame Stock Footage/Shutterstock

The ROI Problem With AI Traffic Nobody Is Measuring Correctly via @sejournal, @DuaneForrester

Search engines were designed to do several things at once: Rank a field of options, route the user to one of them, and keep the human inside the decision so the engine never owned the choice. That last part was not an accident. It was the liability architecture. Large language models were built without any of it. They were built to answer the question directly, which is a different job entirely, and the design choices that follow from it change what visibility looks like, what risk looks like, and what the word ROI can honestly mean when the thing sending you traffic was never built to send traffic in the first place.

Two Systems, Two Jobs

A search engine’s job description is long. It crawls the web, indexes it, ranks a pool of candidate results against a query, presents them as a ranked list, and then waits for the human to make a click decision. The SERP itself has been drifting toward retention for years now, with galleries, rich snippets, answer boxes, local maps, video carousels, and AI Overviews all layering in features that keep the user on the page longer and route fewer of them to third-party sites. But the underlying contract was always the same. The engine offers options. The user selects one. The user owns the choice.

An LLM does not offer options. It produces an answer. The citation, when it appears, is not functioning as a routing instrument. It is closer to a grounding artifact produced by a retrieval pipeline, or in some framings, a confidence hedge, or both at the same time. Whichever read you prefer, none of them describe a system designed to send traffic somewhere else. The system was designed to resolve the question in place.

That distinction sits beneath every metric conversation in this space. When practitioners ask what the LLM referral rate is, what the attributed traffic number looks like, what the click-through from an AI answer is, they are asking questions that assume a routing mechanism that is not actually part of the architecture. Whatever traffic does come through is a byproduct, not a design goal, and confusing the two is the first mistake in almost every conversation about AI visibility ROI.

The Liability Surface Moved

The human in the click decision was the SERP’s shield. If the link the user selected led somewhere harmful, misleading, or defamatory, the engine could point to the list of options and the user’s own agency in choosing one. The engine had not published the claim. It had surfaced 10 candidate sources, the user had chosen one, and whatever happened next was not the engine’s editorial output. That is not a small feature. That is the reason Section 230 protections were structured the way they were, and why algorithmic ranking has traditionally been treated as something other than direct speech.

LLMs have no equivalent shield to stand behind. The system is producing the answer directly, in its own voice, without a field of options or a user-selected source. The liability surface that the SERP was designed to offload sits with the model producing the output, and the cases that have already moved through courts are starting to sketch the edges of that surface.

Walters v. OpenAI was dismissed on summary judgment in May 2025, and the decision leaned heavily on OpenAI’s disclaimers and a sophisticated reader who reasonably knew the chatbot could hallucinate. That reading protects general-purpose consumer chatbots in a very specific kind of case. It does not protect every product that uses a language model. In a separate matter, Air Canada was held liable for its customer service chatbot’s false statements about its own bereavement fare policy, because a customer could reasonably rely on an airline’s branded support agent for accurate information about that airline’s policies. Reasonable reliance is the key legal term, and the more specialized and authoritative the chatbot appears, the harder the disclaimer defense becomes to run.

The active litigation is still mapping the frontier. OpenAI is currently facing multiple lawsuits tied to allegations that ChatGPT drove users toward suicide or harmful delusions, several involving minors. The New York Times copyright case against OpenAI was allowed to proceed by a federal judge in March 2025, and Anthropic settled with book authors in August 2025 for a reported sum well into the billions. European GDPR complaints continue to move through Noyb. Battle v. Microsoft is still live. None of these outcomes are settled, and some will be dismissed on the same disclaimer grounds that resolved Walters. The point is not that LLM operators will lose every case. The point is that the liability surface now sits with the system producing the output, whether the individual plaintiff wins or loses, and every brand building against an LLM inherits some version of that surface when it uses the system’s output in its own customer-facing work.

The Denominator Problem

The most common argument against investing in AI visibility work sounds decisive until you look closely at what it is measuring. The argument runs roughly: ChatGPT and the others send a tiny sliver of referral traffic, somewhere in the low single digits of total inbound, so why reallocate budget toward a channel that barely moves the needle? Conductor’s research pegs the combined AI referral share at about 1% of publisher traffic. That number is real. At first read, it seems to close the ROI question cleanly.

It closes nothing. The problem is the denominator.

While the AI share of traffic holds roughly steady, the absolute volume of search-driven traffic has collapsed across most publisher categories. Similarweb data shows organic traffic to news publishers fell from about 2.3 billion monthly visits in mid-2024 to under 1.7 billion by May 2025, a loss of more than 600 million visits in under a year. Business Insider’s search traffic dropped 55% between April 2022 and April 2025, HuffPost lost roughly half of its search referrals, and The New York Times saw search’s share of its desktop and mobile traffic slide from 44% to 37%. Zero-click searches climbed from 56% to 69% between May 2024 and May 2025 as AI Overviews expanded across the SERP. A Reuters Institute survey of 280 media leaders in late 2025 found they expect another 43% decline on average over the next three years.

Read against that backdrop, a stable percentage share of a shrinking pie is not stable. It is a loss. The skeptics who point at the 1% number are measuring relative share of a traffic base that is contracting underneath them, and they are treating a falling absolute as if it were a steady state. The real question is not whether LLMs are sending meaningful traffic yet. The real question is whether the channel that used to send meaningful traffic is still doing what it used to do, and the answer is visibly no. The denominator is moving, and any ROI calculation anchored to the old denominator is a calculation of the previous environment, not the current one.

What The Billions Say

If the design-intent and liability and denominator arguments still leave room for doubt, the last place to look is revealed preference. What are the companies with the most complete internal data on user behavior actually doing with their capital?

The answer is unambiguous. The five largest U.S. cloud and AI infrastructure providers have committed between 660 and 690 billion dollars in 2026 capital expenditure, nearly doubling 2025 levels. Alphabet alone is guiding to between 175 and 185 billion for 2026, more than doubling its 2025 spend of 91 billion. Microsoft, Amazon, Meta, and Oracle are all running similarly aggressive curves. The number that matters most, and that defuses the usual counter-argument, comes from Bank of America credit strategists who estimate AI capex will reach 94% of operating cash flows in 2025 and 2026, up from 76% in 2024.

That is not the shape of a defensive hedge. A hedge is a fraction of the cash flow, deployed to avoid being caught flat-footed if a competitor’s bet pays off. Companies do not put 94% of operating cash flow into a category for two consecutive years unless the leadership genuinely believes the category is the business. And those leadership teams have access to data that the rest of us do not. They can see inside their own products, their own user behavior shifts, their own cohort analyses, their own enterprise pipeline conversations. They are legally bound to deploy shareholder capital in a way that reflects what they actually see, and what they are deploying it toward is the architecture that produces direct answers rather than ranked lists of options. To believe search-as-we-knew-it remains the gold standard, you have to believe that dozens of CEOs, boards, and senior leadership teams with decades of internal-only data are reading their own numbers wrong, while an external industry with none of that data is reading the market correctly. That does not pencil.

The human-behavior side of the equation makes the same point in a different register. Every labor-saving technology that has ever been introduced has reshaped the status quo faster than its skeptics predicted, because cognitive efficiency is not a preference. It is a survival behavior, wired in through long periods when calories were scarce, and shortcuts mattered. When a new tool appears that makes some task meaningfully easier, adoption is not a matter of whether. It is a matter of how fast and along what curve. ChatGPT is now at roughly 900 million weekly active users, up from 200 million 18 months earlier, and the full category is past a billion active users across platforms. The behavior has already shifted. The money has already shifted. The only thing that has not fully shifted is the measurement frame most practitioners are still using to evaluate the channel.

Which brings the question back to the one that is actually worth asking. What do you do if there is no ROI by the old definition, and you still cannot ignore the channel? The honest answer is that brands will need to invest in visibility work whose return is not expressed in clicks or referral traffic, because clicks and referral traffic are artifacts of the previous design. Being the cited source, the grounded source, the trusted source inside the answer is a different kind of visibility, and it will need a different kind of measurement. The teams that figure that out first will not be doing it because they found an ROI case that convinced their CFO. They will be doing it because they looked at the capex curves, the behavioral curves, and the liability curves, and concluded that the channel is the future, regardless of whether the spreadsheet knows how to score it yet.

If this lands somewhere real in your work, or if it reads wrong from where you are sitting, I would like to hear about it. The shift happening right now is too large for any one practitioner’s vantage point, and the best signal I get comes from the conversations that start after the article ends.

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This post was originally published on Duane Forrester Decodes.

Featured Image: Krot_Studio/Shutterstock; Paulo Bobita/Search Engine Journal

The Whole Point Was The Mess via @sejournal, @pedrodias

Semrush put out an infographic last week. The kind built to be screenshotted into LinkedIn carousels and pasted into webinar decks. Four pillars. The fourth one is called “Technical GEO”: schema, structured data, clean architecture. The line that justifies it: “Ensures AI engines can parse and connect your content.”

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 2 A.M., 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 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 Colors

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

Semrush is already covered. The fourth pillar of its “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%. Sequential H2 to H4 tags double your chances. Short paragraphs make content 49% more likely to appear in AI answers. Perplexity cites UGC in 91% 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 optimization 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 its 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 chief marketing officer.

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 search engine results page 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 Optimize

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 “optimize 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 recognizable disciplines: information architecture, technical writing, readability. They have been recognizable 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 “optimization 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 optimization 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 optimizing 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 behavior 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% 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 optimization, 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 optimizes 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 organized 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 optimized 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 optimizing 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.

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This post was originally published on The Inference.

Featured Image: Roman Samborskyi/Shutterstock