Between August 2022 and February 2023, the population of neural network users in Russia grew dramatically, rising roughly fivefold. This trend was reported by a leading business publication that cited data from the national mobile operator Tele2. The analysis focused on how Tele2 subscribers engaged with free, publicly available online services that rely on artificial intelligence to generate images, music, and text. Notable platforms mentioned include Stable Diffusion, AIVA, Smodin, and others in the same category. It’s important to note that widely known services such as Midjourney, Lensa, and ChatGPT were not counted in these statistics, either because they operate on a paid model or because they were unavailable in the Russian market at the time of the study.
The study identified the most popular AI-powered creative tools among Tele2 users in Russia. Among the top contenders, Mubert emerged as the leading choice for creative generation. Rounding out the top three were ruDALL-E and AIVA, which together captured a significant share of user activity in this space.
In examining user demographics, Tele2’s data revealed a fairly balanced gender distribution among neural network users, with women accounting for 54 percent and men for 46 percent. The most active age group was 31 to 40 years old, suggesting that early to mid-career professionals and tech enthusiasts formed a substantial portion of the audience. Geographically, the highest concentration of users was in Moscow, representing about 18 percent of the user base, followed by Saint Petersburg at around 8 percent. Other notable regional concentrations included Krasnodar with roughly 5 percent, Chelyabinsk near 4 percent, and Perm around 3 percent.
Industry observers attribute the surge in interest to a combination of media attention and the growing presence of AI-focused content in popular messaging channels. Telegram, in particular, has played a role by hosting bots and agents that simplify interactions with AI services, and it is also a primary channel through which algorithm-generated content circulates. This digital ecosystem appears to be driving both awareness and practical usage of neural network tools among the general public.
Earlier discussions about AI-generated imagery and learning processes have highlighted efforts by higher education and research institutions to improve how neural networks are trained. In some cases, institutions are adopting more efficient learning techniques to enhance the capabilities of image generators and other AI-based creators, advancing both speed and quality in output. These developments contribute to the broader adoption trend seen in the Tele2 dataset, underscoring how educational and research-oriented practices intersect with consumer use patterns in real-world settings.