Essay

AI slop SEO is dead — except when you actually do the work

By Axel Antas-Bergkvist Published June 2, 2026

The LinkedIn consensus says scaled AI content is over. Reddit r/SEO repeats the same line every week. Forbes Marketplace lost 43% of its visibility in a few months. The whole “publish a thousand AI articles and watch the traffic roll in” thesis collapsed under Google’s March 2024 update, and most of what’s left of programmatic SEO Twitter is wakes.

Then Casey Hill — CMO at DoWhatWorks — posts on LinkedIn that Zapier is publishing 10+ blog articles a day, dominating inside LLMs, with a SEMrush-reported “AI audience reach” of 2.3 billion.

So which is it? Is AI scaled SEO dead, or is it still winning?

The honest answer is “both, depending on what you mean.” The win condition shifted. The people calling it dead are mostly right about the version they’re describing. The people pointing at Zapier as a counter-example are mostly right about the version Zapier is actually running. And those two versions are almost completely different things.

Here is the unflattering version of the story for everyone involved.

What “AI slop SEO is dead” actually refers to

Google’s policy doesn’t say AI content is bad. It hasn’t said that since February 2023, when Search Central published the line that still anchors everything else: “rewarding high-quality content, however it is produced, is the key.” The 2023 framing was that AI is fine; thin, derivative content meant to manipulate rankings is not. Nothing in the 2024 or 2025 updates contradicted that.

What changed in March 2024 was the policy got teeth. Google added “scaled content abuse” to the spam policies on May 5, 2024, defining it as creating “many pages…for the primary purpose of manipulating Search rankings and not helping users…no matter how it’s created.” The word “no matter” is doing all the work. The policy is method-neutral. Mass-publish thin pages with AI and you get caught. Mass-publish thin pages with a content farm and contractors in 2018 and you would have gotten caught. The thing being punished is the spam pattern, not the production method.

The casualty list is consistent with this read. CNET ran 77 AI-generated finance explainers under “CNET Money Staff” in late 2022, paused after Futurism reported a compound-interest article that mis-stated returns by $10,000, and ended up correcting 41 of 77 stories — a 53% error rate. Sports Illustrated published product reviews under fully fabricated authors with AI-generated headshots; the story broke in November 2023, the fake bylines disappeared overnight, Arena Group’s stock dropped 22%, and the SI title’s licensing arrangement collapsed within months. Forbes Marketplace — affiliate content operated under the forbes.com subfolder since 2020 — lost 43% of its visibility after Google’s May 2024 “site reputation abuse” policy. WSJ Buy-Side lost 77%. CNN Underscored, 63%. Time Stamped, 97%. Generic AI autoblogs Lily Ray tracked across 220+ sites — what Glenn Gabe called “Mount AI” — showed the same curve: rapid growth, then near-total collapse after the March 2024 update.

In all of those cases the failure mode was the same. Content was the product — prose generated to capture queries, with no proprietary data or tool underneath it. Human review was nominal or absent. Scale was the strategy.

That is the version of “AI slop SEO” that is dead. That version has been dead for two years.

What Zapier is actually doing

Now look at Zapier’s site instead of the LinkedIn caricature of it.

Zapier’s blog homepage is, on a typical day, exactly what Hill describes — multiple articles dated the same day, a dense feed of listicles, comparisons, news, and how-tos. May 29, 2026 published four pieces alone: “What are webhooks?”, “What is AI agent orchestration?”, “How to build multi-agent systems with MCP,” and “Why relying on AI content detectors is a bad idea.” The /blog/all-articles/ archive paginates to page 236. At roughly fifteen articles per archive page that implies 3,500+ posts in the editorial blog alone.

But that isn’t where Zapier’s volume actually lives. The editorial blog is a few thousand pieces. The programmatic side — /apps/ — is roughly 50,000 pages, generated as three tiers around every app that joins the directory: an app landing page, an app-to-app integration page, and workflow-specific templates. Independent analysts put /apps/ alone at around 5.8 million monthly organic visits. Add /templates/ and Zapier’s site is in the 6–9 million monthly organic visit range.

The editorial blog and the programmatic /apps/ folder are doing different jobs. /apps/ scales templated distribution of real, proprietary value: the actual list of triggers and actions a given app exposes, the actual integrations the user can build, the actual workflow templates other people have published. The page is templated; the substance is data Zapier has and the open web does not. The same shape of moat as Wise’s currency-conversion pages (live mid-market rates and a calculator behind every USD-to-EUR URL) or Tripadvisor’s location pages (real user reviews, real prices, real photos behind every “things to do in Lisbon” URL). The URL pattern scales. The thing on the page does not collapse into “prose a language model could regenerate from training data.”

The editorial blog runs a different play. Foundation Inc.’s analysis put roughly 100 of Zapier’s listicles at 287,000 ranking keywords driving about 455,000 sessions a month — over half of total blog traffic from about 100 URLs. The whole blog is led by a senior managing editor (Deb Tennen, at Zapier since 2018) and the team has named contributors on the listicles. Read the footers and they’re explicit about the process: “This article was originally published in July 2023 and has also had contributions from David Hartshorne. The most recent update was in March 2026.” Many also include a “How we evaluate and test apps” block, claiming dozens of hours of testing per piece and the line that says the team is “never paid for placement.” Whether you believe all of that or not, it is structurally what an E-E-A-T defense looks like: named humans, dated edits, declared methodology.

That brings us to Wade Foster on Casey Hill’s own podcast in May 2024 — the show is called Angles & Insight, the episode is the SaaStr/Wade Foster sit-down Hill himself ran. Foster’s line about Zapier’s SEO: “It’s not sexy or novel, but it works.” And his warning, on the same episode, about AI content at scale:

A lot of folks try to use AI as a silver bullet, but this type of content generation is the same type of SEO, content mill, and spam that doesn’t cut through.

— Wade Foster, CEO of Zapier, on Casey Hill’s podcast (May 2024)

That’s the Zapier CEO, on the podcast of the person posting on LinkedIn that Zapier is winning at AI-slop SEO, explicitly distancing Zapier from AI-slop SEO. The interesting thing about Hill’s post isn’t that he’s wrong about the volume — the volume is real — it’s that the implication (“look at Zapier scale AI content and crush LLMs”) is the opposite of what Foster said. The volume isn’t AI slop. The volume is real /apps/ data, plus a hundred carefully maintained human-led listicles, plus a comparison shelf, plus daily editorial.

The thing the consensus is missing

The freshness data is where this gets uncomfortable for both camps.

Ahrefs analyzed 17 million AI citations across ChatGPT, Perplexity, Gemini, and Google AI Overviews. The content that gets cited in those AI outputs is, on average, 25.7% fresher than content that ranks organically on Google for the same queries. ChatGPT specifically cites URLs that are 393 to 458 days newer than the top organic results Google would show for the same prompt. Three hundred and ninety-three days newer. That is a lot of years to be off.

The same Ahrefs research found that 76.4% of ChatGPT’s top-cited pages were updated within the last 30 days, and that pages updated in the last three months average about six AI citations versus 3.6 for outdated pages. The classic SEO ranking is still by far the strongest predictor of LLM citation — search rank scored 9.4 out of 10 in May 2026 research on what predicts AI citation, second only to URL accessibility — but among the pages that rank, the fresher ones win the LLM citation game decisively.

This squares with how the LLMs actually work.

Every single URL you see in an LLM output comes from a search engine API (Google or Bing)…LLMs do not rank anything.

— Britney Muller, X (Oct 2025)

ChatGPT pulls from Bing top results. Google AI Overviews pull from Google. Perplexity pulls from a mix. Once the search engine is in the loop, freshness — which the search engines weigh heavily — is in the loop. And the LLMs prefer the fresher ones from among the qualifying set, because their retrieval systems are tuned that way and because a model summarizing five sources is going to lean on the one that’s still current.

This is also where Hill’s “Zapier dominates inside LLMs” claim turns out to be partly right for a reason different from the one his post implies. Independent analyses do show Zapier ranking #1 as a cited source in tech queries inside major LLMs, though only #44 in brand mentions — its URLs surface heavily, its name less so. That’s exactly what you’d expect for a site with strong classical SEO ranking, dense topical coverage, and aggressive refresh discipline: the URLs win the retrieval round, but the brand attribution doesn’t always carry through the summary.

The SEMrush “2.3 billion AI audience reach” number in the LinkedIn post is, separately, a vendor-modeled estimate from SEMrush’s AI Traffic and AI Mode channels. The product launched in November 2025 and extrapolates from SEMrush’s clickstream panel; the panel size and the extrapolation methodology aren’t published. Treat it as a marketing artifact, not a measurement. The real, measurable thing is the rank-and-freshness signal feeding the LLMs, and on that signal Zapier is doing exactly what the Ahrefs citation data predicts wins.

Where this lands

The consensus take (“scaled AI SEO is dead”) and the contrarian take (“look at Zapier”) are both describing real things. They are describing different jobs.

Scaled manufacture of words is dead. The CNET-Sports-Illustrated-Forbes-Marketplace pattern — content as the product, AI as the cheap-labor substitute, scale as the strategy — was already underperforming before March 2024 and is actively penalized now. None of the LinkedIn commentators are wrong about that.

Scaled distribution of proprietary value still works. Wise has eight and a half million currency-pair pages and crushes search because the value isn’t the prose. Tripadvisor’s 700 million-plus URLs work because the value is the reviews. Zapier’s /apps/ folder works because the value is the integration data only Zapier has.

And the third axis — the one the consensus is missing — is scaled maintenance. The pattern that lets Zapier compete in LLM citations isn’t the publishing rate. It’s the refresh discipline. The dated update footers, the named contributors, the “we last tested this in March 2026” signature on the listicles. Ahrefs’ 17 million citation data set says the AI assistants don’t merely prefer freshness — they prefer it by a year. HubSpot’s classic historical-optimization study found refreshing old posts produced a 106% organic lift on average. Brian Dean’s content relaunch case study reported a 260% lift in 14 days. Ahrefs reportedly doubled traffic on a single post by republishing “What is SEO?” The data on refresh-as-strategy has been there for a decade. The LLM era didn’t invent it; it raised the stakes.

The thing that’s hard about refresh isn’t the concept. It’s the operation. Doing it on five posts is easy. Doing it on five hundred — Zapier-style, with consistent dated footers, named human reviewers, methodology blocks, and a process that re-tests claims as the underlying products change — is a real content operation. It is not a “press the button on a content tool” workflow. It is closer to engineering. And on most sites it does not happen, because the existing tooling makes it painful: every refresh is a click-into-the-CMS, edit-in-the-web-editor, save-and-pray loop, repeated for each post.

This is the gap Specter exists in. We don’t generate content. We don’t tell you what to publish next. We pull your existing CMS archive — Ghost, Shopify, WordPress — to local Markdown on your Mac, let you edit with whatever tool you already use (Claude, Cursor, ChatGPT, VS Code, Obsidian, a Python script you wrote one Saturday), and push the reviewed changes back. The dry-run preview shows every change before it lands. The folder of files is exactly what an AI tool wants to work with at scale. The point isn’t to publish faster. The point is to make maintenance an operation rather than a death march. (If you want the longer-form Specter-vs-Zapier breakdown specifically, there’s a page for that.)

Autopilot tools ask “what should we publish?” Specter asks “how do we manage what we’ve already decided to publish?” That framing isn’t a marketing line. It’s the actual job description for the moat the consensus is missing — and the one Zapier has quietly been building this whole time.

If you have a few hundred posts on Ghost, Shopify, or WordPress, the question worth asking isn’t whether to scale AI content production. The interesting question is whether the next twelve months of work is spent publishing two hundred new pieces, or refreshing the two hundred you already have to the standard Zapier holds the bottom of every listicle to. The freshness data says the second one wins. The Forbes Marketplace data says the first one is the trap.

Sources

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