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Ranking the Top 100 Websites in the World

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As a greater portion of the world begins to live more of their life online, the world’s top 100 websites continue to see explosive growth in their traffic numbers.

To claim even the 100th spot in this ranking, your website would need around 350 million visits in a single month. Using data from SimilarWeb, we’ve visually mapped out the top 100 biggest websites on the internet. Examining the ranking reveals a lot about how people around the world search for information, which services they use, and how they spend time online.

Note: This is a ranking of biggest websites, specifically. Brands that extend across platforms or serve the majority of their users through an app will not necessarily rank well on this list. As a result, you’ll notice the absence of companies like WeChat and Snapchat.

The Top 100 Websites

The 100 biggest websites generated a staggering 206 billion visits in June 2019. Google, YouTube, and Facebook took the top spots, followed by Baidu and Wikipedia. Below is the full ranking:

Global RankDomainMonthly visits (billions)ParentCountry
1Google.com60.49Alphabet Inc🇺🇸 United States
2Youtube.com 24.31Alphabet Inc🇺🇸 United States
3Facebook.com19.98Facebook, Inc🇺🇸 United States
4Baidu.com9.77Baidu, Inc🇨🇳 China
5Wikipedia.org4.69Wikimedia Foundation🇺🇸 United States
6Twitter.com3.92Twitter, Inc🇺🇸 United States
7Yahoo.com3.74Verizon Comm. Inc🇺🇸 United States
8pornhub.com3.36Mindgeek🇨🇦 Canada
9Instagram.com3.21Facebook, Inc🇺🇸 United States
10xvideos.com3.19WGCZ Holding🇨🇿 Czech Republic
11yandex.ru3.06Yandex🇷🇺 Russia
12ampproject.org2.76N/A🇺🇸 United States
13xnxx.com2.47WGCZ Holding🇨🇿 Czech Republic
14amazon.com2.41Amazon.com, Inc🇺🇸 United States
15live.com2.25Microsoft Corporation🇺🇸 United States
16vk.com2.16Mail.ru Group🇷🇺 Russia
17netflix.com1.81Netflix, Inc🇺🇸 United States
18qq.com1.76Tencent🇨🇳 China
19whatsapp.com1.76Facebook, Inc🇺🇸 United States
20mail.ru1.64Mail.ru Group🇷🇺 Russia
21Reddit.com1.55Advance Publications🇺🇸 United States
22yahoo.co.jp1.5Verizon Comm. Inc🇯🇵 Japan
23google.com.br1.38Alphabet Inc🇧🇷 Brazil
24bing.com1.32Microsoft Corporation🇺🇸 United States
25ok.ru1.08Mail.ru Group🇷🇺 Russia
26xhamster.com1.06Hammy Media Ltd🇨🇾 Cyprus
27sogou.com1Tencent, Sohu Inc🇨🇳 China
28ebay.com0.95eBay Inc🇺🇸 United States
29bit.ly0.95Spectrum Equity🇺🇸 United States
30twitch.tv0.91Amazon.com, Inc🇺🇸 United States
31linkedin.com0.91Microsoft Corporation🇺🇸 United States
32samsung.com0.89Samsung Group🇰🇷 South Korea
33sm.cn0.81Alibaba Group🇨🇳 China
34msn.com0.8Microsoft Corporation🇺🇸 United States
35office.com0.79Microsoft Corporation🇺🇸 United States
36globo.com0.74Grupo Globo🇧🇷 Brazil
37taobao.com0.74Alibaba Group🇨🇳 China
38pinterest.com0.74Pinterest, Inc🇺🇸 United States
39google.de0.73Alphabet Inc🇩🇪 Germany
40Microsoft.com0.72Microsoft Corporation🇺🇸 United States
41accuweather.com0.71AccuWeather Inc🇺🇸 United States
42naver.com0.64Naver Corporation🇰🇷 South Korea
43aliexpress.com0.64Alibaba Group🇨🇳 China
44fandom.com0.61Wikia Inc🇺🇸 United States
45quora.com0.58Quora Inc🇺🇸 United States
46github.com0.57Microsoft Corporation🇺🇸 United States
47imdb.com0.57Amazon.com, Inc🇺🇸 United States
48uol.com.br0.56Grupo Folha🇧🇷 Brazil
49docomo.ne.jp0.56Tata Teleservices🇯🇵 Japan
50youporn.com0.55Mindgeek🇨🇦 Canada
51bbc.co.uk0.55Public owned🇬🇧 United Kingdom
52microsoftonline.com0.55Unknown🏴 Unknown
53paypal.com0.53Paypal🇺🇸 United States
54google.fr0.53Alphabet Inc🇫🇷 France
55yidianzixun.com0.51Particle Inc🇨🇳 China
56wordpress.com0.51Automattic🇺🇸 United States
57news.google.com0.51Alphabet Inc🇺🇸 United States
58sohu.com0.51Sohu🇨🇳 China
59duckduckgo.com0.51Duck Duck Go, Inc🇺🇸 United States
60google.co.uk0.51Alphabet Inc🇬🇧 United Kingdom
6110086.cn0.5China Mobile🇨🇳 China
62iqiyi.com0.5Baidu, Inc🇨🇳 China
63booking.com0.5Booking Holdings🇺🇸 United States
64amazon.co.jp0.49Amazon.com, Inc🇯🇵 Japan
65cricbuzz.com0.49Times Internet🇮🇳 India
66taboola.com0.48Taboola Inc🇺🇸 United States
67amazon.de0.48Amazon.com, Inc🇩🇪 Germany
68cnn.com0.47Turner Broadcasting🇺🇸 United States
69jd.com0.47Various (Tencent 20%)🇨🇳 China
70apple.com0.47Apple Inc🇺🇸 United States
71google.it0.45Alphabet Inc🇮🇹 Italy
72bilibili.com0.44Bilibili Inc🇨🇳 China
73google.co.jp0.44Alphabet Inc🇯🇵 Japan
74livejasmin.com0.44Docler Group🇱🇺 Luxembourg
75tmall.com0.44Alibaba Group🇨🇳 China
76news.yahoo.co.jp0.44Verizon Comm. Inc🇯🇵 Japan
77youtu.be0.44Alphabet Inc🇺🇸 United States
78tribunnews.com0.43Kompas Gramedia Group🇮🇩 Indonesia
79amazon.co.uk0.43Amazon.com, Inc🇬🇧 United Kingdom
80chaturbate.com0.43Multi Media LLC🇺🇸 United States
81google.co.in0.41Alphabet Inc🇮🇳 India
82craigslist.org0.41Craigslist🇺🇸 United States
83imgur.com0.41Imgur Inc🇺🇸 United States
84bbc.com0.41Public owned🇬🇧 United Kingdom
85fc2.com0.39FC2, Inc🇺🇸 United States
86tsyndicate.com0.39Unknown🏴 Unknown
87redtube.com0.38Mindgeek🇨🇦 Canada
88tumblr.com0.37Verizon🇺🇸 United States
89foxnews.com0.36Fox Corporation🇺🇸 United States
90rakuten.co.jp0.36Rakuten Inc🇯🇵 Japan
91google.es0.36Alphabet Inc🇪🇸 Spain
92outbrain.com0.36Outbrain Inc🇺🇸 United States
93discordapp.com0.36Various🇺🇸 United States
94amazon.in0.35Amazon.com, Inc🇮🇳 India
95crptgate.com0.34Unknown🏴 Unknown
96weather.com0.34Landmark Media Enterprises, LLC🇺🇸 United States
97toutiao.com0.34Bytedance🇨🇳 China
98youku.com0.34Alibaba Group🇨🇳 China
99adobe.com0.34Adobe Inc🇺🇸 United States
100news.yandex.ru0.33Yandex🇷🇺 Russia

Search Reigns Supreme

Search engines provide the connective tissue that binds the internet together, and they accounted for the majority of website traffic in the top 100 ranking.

Google is the undisputed top website in nearly every country in the world. In fact, Alphabet’s 11 domains in the top 100 ranking – including YouTube and a number of international versions of Google – racked up an impressive 90 billion visits in a single month.

Exceptions to Google’s dominance can be found in China (Baidu) and Russia (Yandex), where homegrown search engines have managed to capture the domestic market.

One scrappy competitor, DuckDuckGo, is slowly gaining prominence as an alternative to Google. The search engine’s focus on user privacy appears to be resonating with internet users as the site’s traffic has surpassed 500 million visits per month.

Full Stream Ahead

Video streaming and sharing is another major driver of global internet traffic.

Thanks to high-powered phones and bigger data plans, video is now a prominent portion of internet content consumption. This can take a few forms, from binge watching TV shows on Netflix to short-form video uploads on platforms like Douyin and Instagram.

Live streaming is increasingly a bigger part of the mix. Twitch, which is focused on gaming, is now ranked 30th in the world in web traffic. The Amazon-owned platform is now so popular that on any given night, its viewership surpasses many of the major U.S. cable networks.

Hours watched on Twitch

Of course, this category also includes adult content, which is well represented in this ranking. XNXX, XVideos, and PornHub all made the top 20, and the three websites combined for over nine billion visits in the most recent month of data available.

Old Dogs, New Tricks

Classic web portals such as MSN and Yahoo are still putting up impressive traffic numbers, but major players are increasingly staying relevant by acquiring rising internet stars.

In the case of Microsoft, acquiring Github and Linkedin helped the company target new markets and grow their overall presence online. Amazon’s acquisition of Twitch proved to be a good bet, and Instagram continues to breathe new life into Facebook, which has seen a backlash focused on its original namesake social network.

Google isn’t sitting still either. The company recently championed the open-source AMP Project to help improve the performance of mobile pages, which are increasingly bogged down by adware, unoptimized images, and JavaScript. In a short amount of time, the AMP Project has taken off to become one of the biggest websites in the world.

The project is not without controversy though.

Critics point out that cached AMP pages – which are hosted by Google – essentially cut out content creators, and that non-compliant pages may lose their ranking on mobile search results. As the project moves towards becoming a foundation, it remains to be seen how AMP will evolve and how much involvement Google will have in the future.

The Geography of the Top 100 Websites

The internet may be a global network, but many of the gatekeepers are still located in the United States. If international domain suffixes of companies like Amazon and Google are counted, 60 of the 100 websites in the ranking are American.

Below is a breakdown of the Top 100 by country.

Top 100 Websites Ranking by Country

China is a strong runner-up, with 15 websites in the Top 100. While most of these Chinese companies are focused on the sizable domestic market, some are also making global inroads through investment. Tencent has partially backed the fast-growing chat platform, Discord, and it also has double-digit stakes in Snapchat and Spotify.

With the exception of Baidu, all of the biggest websites in the world have swelled in size by serving a global audience. As the tech market continues to mature in China, it remains to be seen whether Chinese companies can successfully move beyond the firewall to become the next Facebook or Google.

Correction: Bilibili, a website run by a Chinese company, was incorrectly identified as a Japanese company.

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AI

Charted: The Exponential Growth in AI Computation

In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.

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A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

Charted: The Exponential Growth in AI Computation

Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?

This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.

The Three Eras of AI Computation

In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.

Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.

PeriodEraCompute Doubling
1950–2010Pre-Deep Learning18–24 months
2010–2016Deep Learning5–7 months
2016–2022Large-scale models11 months

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.

With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.

Predicting AI Computation Progress

Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.

For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.

Here’s a list of important AI models through history and the amount of compute used to train them.

AIYearFLOPs
Theseus195040
Perceptron Mark I1957–58695,000
Neocognitron1980228 million
NetTalk198781 billion
TD-Gammon199218 trillion
NPLM20031.1 petaFLOPs
AlexNet2012470 petaFLOPs
AlphaGo20161.9 million petaFLOPs
GPT-32020314 million petaFLOPs
Minerva20222.7 billion petaFLOPs

Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.

It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.

However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.

Where Does This Data Come From?

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.

Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.

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