In the five school-season months of March through July 2024, English Wikipedia's article on osmosis was read about 154,000 times by humans. In the same five months of 2026, it was read about 56,000 times. Two years, and nearly two-thirds of its readers are gone.
Over the same window, the article on the Antikythera mechanism, the corroded bronze gearwork that turned out to be a two-thousand-year-old astronomical computer, gained readers: up 3.5 percent. So did the Peloponnesian War, up 5.4 percent.
Hold those three facts together and you are most of the way to understanding what is actually happening to the web's greatest reference work, because the difference between them is not popularity. It is the shape of the visit. "Osmosis" is a question, and questions now get answered before anyone reaches Wikipedia. "Antikythera mechanism" is an afternoon, and afternoons still belong to the page.
On 17 October 2025, Marshall Miller of the Wikimedia Foundation published a post on the foundation's blog with an unusual pair of admissions. Human pageviews were down "roughly 8% as compared to the same months in 2024." And the foundation had discovered, while investigating, that some of what it had been counting as human wasn't: "Around May 2025, we began observing unusually high amounts of apparently human traffic, mostly originating from Brazil," which turned out to be bots built to evade detection. After reclassifying March through August 2025 with updated logic, the real human decline emerged. The post attributed it to generative AI and social media changing how people seek information, "especially with search engines providing answers directly to searchers," and it carried a methodological caveat that deserves framing: "Revising our data in this way means we have to interpret it with care, as our bot detection systems apply different rules at different points in time."
Here is the good news for anyone who prefers checking to quoting: Wikipedia's pageview data is public, per-article, split by agent type, back a decade, served from an open API that requires no key. So instead of restating the foundation's number, we queried the API and computed the thing the coverage never did: the decline segmented by what kind of article a page is. Every figure below comes from those queries, the reproduction recipe is in the footer, and the aggregate number checks out first: for the foundation's own window, our human-pageview computation lands at -7.0 percent against their "roughly 8," the small gap explained by our reading today's fully reclassified data.
Then it gets more interesting than the headline.
Sort articles into three rough classes. Reference-lookup pages, the "what is X" class whose entire job an AI summary can absorb: osmosis, mitosis, the Pythagorean theorem, standard deviation. News- and event-driven pages: NATO, inflation. And long-tail deep dives, pages nobody visits by accident: the Byzantine Empire, the Battle of Kursk, the Antikythera mechanism.
Comparing March-July 2026 against the same months of 2024, human pageviews in our sample fell 48.2 percent for the lookup class, 38.4 percent for the news class, and 17.3 percent for the deep dives, against a site-wide decline of 13.2 percent. The lookup class fell at roughly three and a half times the site-wide rate. A clean, monotonic gradient, in exactly the order the answer-engine hypothesis predicts: the more completely a summary can do a page's job, the harder that page fell.
The per-article numbers are starker than the class averages, and the spread inside each class is itself informative. Standard deviation, down 59.8 percent. Mitosis, down 52.3. The Pythagorean theorem, down 49.4. But photosynthesis, a sibling homework topic, fell only 23.7, and supply and demand only 19.2, a reminder that even within the question-shaped class, pages differ in how much of their traffic was ever really one-line lookups. The news class has the same texture: NATO down 53.5 and inflation down 51.6, both topics a summary handles fluently, while the Byzantine Empire, closer to reading than to checking, held to a 27.9 percent loss and the Battle of Kursk to 19.4. The gradient is not a cliff between categories; it is a slope that tracks, page by page, how much of each article's job could migrate upstream. And then the two pages that grew, which are the paragraph that matters: the Antikythera mechanism and the Peloponnesian War gained readers through the exact window in which osmosis lost two-thirds of its audience. Nobody asks an answer engine for the Antikythera mechanism in one line, because the reason you are there is that you want to stay a while. The pages bleeding readers are the ones whose job was answering a question that now gets answered upstream, by systems trained, in meaningful part, on those very pages.
Now the honesty this result requires, stated at full volume rather than in a footnote. This is a sample of eighteen hand-picked articles: eight lookup, five news, five deep-dive, chosen to fit the class definitions before their numbers were pulled, but chosen. The gradient is suggestive, not established; establishing it as a population statistic needs a large random sample drawn per class. And the confound has a name: the lookup class is the school class. Osmosis and the Pythagorean theorem are homework, and a shift in how students do homework would produce this exact gradient whether or not it runs through AI summaries. The attribution to answer engines is inference, consistent with the data rather than isolated by it. One methodological point runs the other way and is worth knowing: the foundation's reclassification applied site-wide, so while any absolute level in this data deserves suspicion, a difference between classes measured the same way is considerably more robust than any single number in it.
The second computation is simpler: the monthly year-over-year change in human pageviews, every month since the decline began. It has now been negative for twenty-seven consecutive months. The worst month in the entire series is not in the foundation's reported window at all. It is March 2026, at -12.2 percent, five months after the coverage cycle ended. For March-July 2026 against 2024, the site-wide human decline is 13.2 percent, roughly double the figure that made the news.
And buried in that monthly table is a trap we want to spring deliberately, because it is a perfect specimen of a failure every metrics owner should recognize. Read naively, early 2026 looks dramatic, at minus ten to twelve percent, and mid-2026 looks like recovery, softening to minus three. But the foundation reclassified only March through August 2025. A January 2026 reading is therefore measured against a January 2025 base that never got cleaned and likely still contains the disguised bots, inflating the base and overstating the decline; an April 2026 reading is measured against a cleaned, lower base. Part of the "deepened, then recovered" shape is neither deepening nor recovery. It is the seam between two definitions of "human," showing up in a trend line, exactly as the foundation's own caveat warned. A definitional change masquerading as a trend, sitting inside the very dataset we are using to measure a real trend, is not a reason to distrust the exercise. It is the second subject of the exercise.
The tidy version of this story says the crawlers feasted while the readers left. The data declines to say that.
The API splits traffic three ways: user, spider for declared crawlers, and automated for detected bots. Declared spider traffic did not rise to meet the answer engines; it fell 27.2 percent over our two-year window and now sits below its 2021 level. The automated bucket went the other way: up 28.7 percent over the window, and up roughly 79 percent against its 2021-2023 baseline. Whatever is fetching Wikipedia at scale in 2026 is not doing it under a polite crawler's declared user-agent.
But before anyone builds a narrative on either curve, the confound: spider traffic fell off a cliff between September and October 2025, which is precisely when the foundation re-engineered its bot detection. A change in the classifier explains that step at least as well as a change in crawler behavior, and three of the four traffic series have that definitional seam running through the middle of the measurement window. So the honest summary of the machine side is this: we can measure that the humans left, and we can measure which pages they left. We cannot cleanly measure what the machines did, because the instrument that counts machines was rebuilt mid-window by the same organization reporting the decline. The extraction asymmetry, the encyclopedia feeding an answer layer that starves it, survives in the data, but only in its careful form: human reading down and graded by absorbability, undeclared automation up sharply, declared crawling ambiguous under a moved instrument.
One more specimen for the cabinet of measurement humility: as a control, we tried the Main Page. Its "human" pageviews rose 49.6 percent over the window, from 746 million to over 1.1 billion in five months, which is implausible as a change in reading and almost certainly an artifact of apps and default loads. A single page can be counted in ways that have nothing to do with readers. We report it and use it for nothing, which is what you should do when a control misbehaves.
For the web, the reading is sober enough. The encyclopedia's question-shaped pages funded a virtuous loop for twenty years: a student's quick lookup was also a donation impression, a future editor's first visit, a citation followed. The answer layer absorbs precisely those visits first, while the afternoon-shaped pages keep their people. Whether what remains sustains the commons that the answer layer itself is trained on is now a live question, and the monthly series says it is not stabilizing yet. Wikipedia's own community has felt the tension from both directions at once: in June 2025 the foundation paused a pilot that would have put AI-generated summaries atop articles after its volunteer editors revolted, which means the institution losing readers to machine summaries elsewhere declined, under internal protest, to serve machine summaries itself. Whatever the right strategy is, the people who write the encyclopedia have made their position on becoming their own answer engine clear.
For anyone who runs a product with metrics, this dataset is a masterclass you can rerun in an afternoon, and it teaches three disciplines. Segment before you attribute: the site-wide 13 percent hides a spread from minus 60 to plus 5, and any strategy tuned to the aggregate is tuned to a page that does not exist; your surfaces divide into questions and afternoons too, and they are not declining together. Map your instrument's seams before reading trends across them: every reclassification, every bot-filter update, every tracking change is a discontinuity that will impersonate a trend, and the only defense is knowing the dates. And when a partner ecosystem starts answering your users upstream of you, expect the first losses exactly where the visit was shortest, which is often where the aggregate is thickest and the alarm is quietest.
The whole analysis stands on an open API, two scripts, and the willingness to check a widely quoted number instead of repeating it. The number held. The story underneath it was bigger.
Method and sources: Wikimedia Pageviews REST API (wikimedia.org/api/rest_v1), aggregate endpoints for en.wikipedia by agent type (user/spider/automated, monthly, 2021-2026) and per-article monthly series for 21 articles, 2023-2026; windows compared are March-July sums, 2026 vs 2024, human (user) agent unless stated; 18 hand-picked articles carry the class result (8 lookup, 5 news, 5 deep-dive), stated as suggestive, not population-level. Marshall Miller, "New user trends on Wikipedia," Wikimedia Foundation (diff.wikimedia.org), 17 October 2025, for the reported ~8%, the Brazil bot reclassification, and the different-rules-at-different-times caveat.
If you publish a number other people act on, the seam problem in this piece is yours too: a reclassification, a bot-filter update, a tracking change, each one a discontinuity that will impersonate a trend to anyone reading the series later. The only defence is a record of when the definition moved and why. Chain of Consciousness keeps that reasoning attached to the result, so a later reader can tell a change in the world from a change in the instrument.
pip install chain-of-consciousness
npm install chain-of-consciousness