In November 2022, OpenAI released ChatGPT to the public for the first time. Less than four years later, around half of U.S. adults say they use chatbots powered by artificial intelligence, including 24% who say they use them daily. These tools’ ability to generate human-sounding text has raised a basic question about the modern web: How much content online is now written by AI rather than by other people?
To explore this question, we used the Common Crawl web archive to collect almost half a million English-language webpages from the past five years – starting a couple of years before the release of ChatGPT. We then ran the text of those pages through an AI detection tool called Open Pangram to see how many of them were likely written or substantially edited by AI.
These dots represent a random sample of 10,000 webpages collected in July 2026. Together they make up a snapshot of what the English-language internet looked like at that point in time.
Using a machine learning model, we checked the text of each webpage for signs of AI authorship. The model looks at patterns in language, identifying words, phrases and linguistic quirks that are more commonly used by AI than by human authors.
Of all the pages in this sample, 10% show significant signs of AI authorship.
This is part of an upward trend that began in late 2022, when ChatGPT was first released, and continued as other AI chatbots such as Claude and Gemini followed. As time goes on, the share of webpages showing signs of AI authorship is increasing.
One-in-ten webpages as of July 2026 may seem modest. But the internet includes a mix of new and old material. A lot of pages in these random samples couldn’t have been written by AI.
If we filter old webpages out of our samples and look at only the pages published after the release of ChatGPT, the trend is even more pronounced.
In the July 2026 snapshot, signs of AI authorship can be found in over one-third of pages published after ChatGPT was released. This is in line with other studies that have shown that large shares of recently published pages on the internet were likely written or substantially edited by AI.
Where online is AI-authored text most common?
AI-authored text is not evenly spread across the web. When ChatGPT was first released, the kinds of linguistic patterns that can signal AI authorship appeared at similar rates across the main top-level web domains (.com, .org, .edu and .gov).
But in samples from 2026, around one-in-ten pages with a .com domain show signs of AI authorship – about double the share on .org domains (4.6%), and 10 times the rate on .edu or .gov domains (both around 1%).
| Crawl date | .com | .org | .edu | .gov | n_com | n_org | n_edu | n_gov |
|---|---|---|---|---|---|---|---|---|
| 2021-01-01 | 1.09 | 0.83 | 0.57 | 0.41 | 29366 | 4850 | 1993 | 445 |
| 2021-07-01 | 1.04 | 1.02 | 0.34 | 0.23 | 23202 | 3828 | 1713 | 407 |
| 2022-01-01 | 1.08 | 0.68 | 0.51 | 0.39 | 11407 | 1924 | 804 | 243 |
| 2022-07-01 | 1.07 | 0.63 | 0.64 | 0.25 | 23734 | 4020 | 1756 | 431 |
| 2023-01-01 | 1.63 | 0.9 | 0.56 | 0.34 | 18631 | 3093 | 1238 | 305 |
| 2023-07-01 | 2.76 | 1.89 | 0.28 | 1 | 12047 | 1925 | 703 | 200 |
| 2024-01-01 | 3.76 | 2 | 1.7 | 1.42 | 23739 | 3590 | 1180 | 328 |
| 2024-07-01 | 4.7 | 2.1 | 0.57 | 1.72 | 35976 | 5049 | 1589 | 545 |
| 2025-01-01 | 5.48 | 2.89 | 0.95 | 1.62 | 35766 | 5012 | 1559 | 536 |
| 2025-07-01 | 6.64 | 2.85 | 1.02 | 1.4 | 34971 | 4727 | 1329 | 509 |
| 2026-01-01 | 9.35 | 4.59 | 1.03 | 0.76 | 41116 | 6363 | 1571 | 669 |
What are some common features of AI authored text?
AI detection models aren’t perfect – they sometimes misclassify individual documents that were written by humans as including signs of AI authorship, and vice versa. But if we look at very large collections of texts together, we can start to see that certain types of punctuation, words and phrases show up at much higher rates in AI-generated content than in text written by humans.
WRITTEN BY AI
The internet has evolved into something far beyond a simple network of connected pages — it’s a living, shifting ecosystem shaped by algorithms, creators, and audiences alike, bolstered by waves of AI-generated content that blur the lines between human and machine expression. As users keep delving in to endless streams of posts, videos, and synthetic voices, a pivotal question emerges about authenticity, ownership, and trust — because it’s not just information, it’s influence.
- Em dashes
- Oxford commas
- AI vocabulary
- Negative parallelism
AI models are trained on large datasets of human writing, and they learn to mimic the patterns and styles of that writing. Sometimes certain types of writing are overrepresented in that training process. As a result, models trained on that data can end up using certain words, phrases or linguistic quirks more than humans typically do.
For instance, lots of journalistic and academic writing uses the em dash (—), which is a punctuation mark used to indicate a break in thought or set off a parenthetical statement. AI models tend to use these dashes a lot more than humans typically do. They’re also more likely than human authors to list items in threes and to use Oxford commas in lists.
And as more AI-generated content appears online, these and other AI “tells” have become more common across webpages. Comparing the internet of today to a snapshot from 2023:
- Em dashes appear about twice as frequently.
- Oxford commas see a 63% increase.
- Certain words that AI models like to use (such as “delve,” “interplay” or “testament”) have more than doubled in usage.
- Using “negative parallelism” to structure a comparison (“it’s not just X, it’s Y”) has nearly tripled – although this is still fairly rare overall.
| Date | Em dash (per 10k words) | Oxford comma (per 10k words) | AI vocabulary (per 10k words) | Negative parallelism (per 10k words) |
|---|---|---|---|---|
| 2023-01-01 | 5.79 | 34.04 | 11.94 | 0.87 |
| 2023-07-01 | 4.2 | 42.57 | 21.42 | 1.88 |
| 2024-01-01 | 5.32 | 46.25 | 25.66 | 1.9 |
| 2024-07-01 | 4.58 | 44.06 | 23.56 | 2.08 |
| 2025-01-01 | 6.3 | 48.35 | 24.79 | 2.52 |
| 2025-07-01 | 8.58 | 51.08 | 28.15 | 2.72 |
| 2026-01-01 | 11.19 | 55.51 | 26.02 | 2.36 |
Of course, em dashes or Oxford commas on their own don’t necessarily mean a particular piece of writing was produced using AI – humans use these in their writing too! But if we look at a lot of documents together, we can start to see patterns in how often human authors and AI models reach for these features on average. And sophisticated detection models like the one we used for this analysis have more to go on than just looking for a few telltale signals. They also learn to recognize more subtle statistical patterns in word choice and sentence structure which help them determine whether a particular piece of writing was likely AI-authored.
Acknowledgments
This analysis was conducted by Samuel Bestvater, senior data scientist, who also wrote this essay. Carson TerBush, associate information graphics designer, created the graphics and animations and contributed to web development, assisted by Chris Baranovski, lead engineer – editorial content. Janakee Chavda, associate digital producer, produced the report for web. Analysis code was reviewed by Skyler Seets, computational social science assistant, and the essay was checked by Anna Lieb, computational social science analyst. It was copy edited by Anna Jackson, editorial specialist. Aaron Smith, director of Data Labs research, provided editorial guidance.