Yandex Reveals How Russians Utilize Neural Networks Across Different Age Groups

A recent study conducted by Yandex has unveiled the diverse applications of neural networks among Russian users, highlighting significant variations in usage patterns across different age brackets. The research, which aimed to understand how Russians are integrating neural network technologies into their daily routines, found that the utility of such technologies largely varies with the user’s age.

Young adults aged 18-25 are leveraging neural networks predominantly for academic and programming tasks, including the creation of presentations, writing student papers, coding, solving various problems, and answering questions. This demographic shows a keen interest in utilizing these advanced technologies to enhance their educational and early professional endeavors.

The 25-34 age group, which likely encompasses young professionals, focuses on using neural networks for branding and social media purposes. They employ these technologies to create logos, social media posts, product cards, content plans, and generate brand names, indicating a shift towards entrepreneurial and marketing applications.

Individuals aged 35-44 are utilizing neural networks for both professional and creative tasks such as creating product cards, social media posts, interior designing, and video production and processing. This suggests a blend of professional, creative, and personal use, reflecting the versatility of neural networks in supporting various aspects of life.

For those aged 45-54, neural networks serve more artistic and creative functions, including writing music, processing images, generating images from photographs, writing articles, and animating pictures. This age group appears to explore the potential of neural networks to enhance their creative output and personal projects.

The study also found that users over 55 years old use neural networks for photograph enhancement, poetry writing, face changing in photographs, and restoring old photographs. This indicates a preference for neural networks in preserving memories and engaging in creative writing.

Furthermore, the study revealed gender differences in neural network usage, with a higher percentage of men using these technologies for text generation and image creation compared to women. Men also exhibit a stronger belief in the potential and future of neural networks, whereas women are more likely to view them as a form of temporary entertainment.

The analysis of Yandex Search queries showed a high interest in understanding neural networks, with over half of the inquiries focused on learning about their functionality and applications. A significant portion of users are curious about how to utilize, create, and monetize neural networks.

This comprehensive study not only sheds light on the widespread interest and varied applications of neural networks among Russians but also underscores the evolving relationship between humans and artificial intelligence technologies. Despite the popularity of neural networks, it’s noted that services like Google’s Gemini 1.5 Pro neural network are unavailable in Russia, indicating a gap in access to global neural network technologies.

Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.