In November 2022, OpenAI pushed ChatGPT into the public spotlight. Less than four years later, the Pew Research Center pulled apart half a decade of English-language webpages and asked a plain question: how much of the modern internet is already written by machines?

Where the data came from: 490,000 Common Crawl pages and one detector

A team led by Samuel Bestvater, senior data scientist at Pew's Data Labs, drew roughly 490,000 English-language pages from Common Crawl, the open web archive, spanning January 2021 through July 2026 — roughly two years before ChatGPT's public release through the present. Every page was scored by an open-weight detection model called Open Pangram, built by Pangram Labs. The model's logic is statistical: it looks for distributional differences between how large language models write and how human authors write, then flags pages where the AI fingerprint is clearly visible.

The study is large sample, explicit methodology, explicit tool. Pew published the full data essay on August 20, 2026, and released the underlying CSV.

Three numbers that matter: 10%, one-third, tenfold

The headline number: across the full sample window, the July 2026 snapshot found that 10% of pages show significant signs of AI authorship. That sounds modest until you remember that web crawls mix in lots of pre-ChatGPT material. Pew sharpened the lens further, filtering down to pages published after ChatGPT's release in late November 2022. In that subset, more than one-third (over 33%) of pages show clear AI writing fingerprints.

The split across top-level domains is the sharpest signal. In 2026 samples, roughly one in ten .com pages showed AI authorship signs, almost double the .org rate (4.6%) and about ten times the .edu and .gov rates (both around 1%). In other words, the commercial web is where AI content lives; academic and government domains are still leaning on human-reviewed writing.

The fingerprints AI writers leave behind: em dash, Oxford commas, negative parallelism

Pew also tracked how AI-favored language features have shifted at the corpus level. Compared with the 2023 sample, by 2026:

  • Em dashes (—) appear at roughly twice the rate.
  • Oxford commas have risen by 63% in usage frequency.
  • AI-vocabulary tokens like "delve," "interplay," and "testament" more than doubled.
  • Negative parallelism ("it is not just X, it is Y") nearly tripled, though still rare in absolute terms.

Open Pangram is not relying on any single tic. The detector looks at multi-dimensional statistical drift. But at corpus scale these signals stack up into reliable AI fingerprints.

Detection is not infallible: error and adversarial drift

Pew is careful to flag that detection models routinely mislabel human work as AI, and miss AI work dressed up as human. Generators also evolve. The current Pangram model is built to read statistical patterns across many small signals, which means the same passage could score differently in 2026 than in 2027 as both sides keep moving.

Pew released the data and the detector openly so researchers, newsrooms, and regulators can rerun and iterate, instead of trusting any single black-box score.

What this means for the content ecosystem: stratification accelerates

The real signal behind the numbers is the accelerating stratification of information sources. .edu and .gov domains are still at 1% AI content, which means those institutions mostly run human-gated writing. Commercial .com domains are running at roughly ten times that rate, meaning more and more of what readers see on the open web is assembly-line output.

This stratification is bad for consumers in the short run. Search engines and aggregators do not flag "this page is likely machine-written" by default. In the longer run, it forces content platforms, subscription services, and SEO / anti-AI detection vendors into new contracts: publishers have to disclose AI-assist use, platforms have to ship AI-trace filters, and on the reader side "I want to read something written by a human" becomes a new filter dimension.

So what: stop asking "can machines write" and start asking "how do I know this was written by one"

The most useful move in this study is not the final AI-percentage headline. It is the decision to put the detection tool and the dataset on the table. Common Crawl is public, Open Pangram is open-weight, and the per-period percentages are downloadable. When we casually say "AI can write anything now," this 490,000-page controlled comparison offers a more precise counterpoint: between can write and detected as AI-written there is still a quantifiable gap, and that gap is both a technical problem and a governance problem.

Over the next year or two, the same dataset at a later timestamp will be the line to watch. Does the AI-writing share keep climbing, or does it bend once a platform or rule puts pressure on the curve? And does the detector keep up with the generator? Whichever curve bends first will shape what "a machine-written internet" looks like in everyday reading.

Reference: Pew Research Center, "How Much of the Internet Is Written With AI?", August 20, 2026. Original link via NewsForAI.