Over 100 Illegal Mining Farms Found in the North Caucasus Since the Beginning of the Year

RBK-cryptoPublicado a 2025-12-11Actualizado a 2025-12-11

Resumen

Since the beginning of 2025, over 100 illegal cryptocurrency mining farms have been discovered in Russia's North Caucasus Federal District, according to "Rosseti Severny Kavkaz." The total financial damage from their operations is estimated at over 656 million rubles. Although cryptocurrency mining is officially banned in the region until Spring 2031, many illegal miners continue to operate by stealing electricity, either bypassing meters or connecting directly to the grid without contracts. The breakdown of discovered farms by region is as follows: 79 in Dagestan (causing 89.5 million rubles in damage), 14 in Ingushetia (455.5 million rubles), 5 in Karachay-Cherkessia (104.1 million rubles), 2 in Stavropol Krai (1 million rubles), 2 in North Ossetia (390,000 rubles), and 1 in Kabardino-Balkaria (5.8 million rubles). Dagestan leads in the number of cases, which the energy company attributes to individuals attempting to generate profit without using their own funds for electricity. In response to the widespread issue, Russian Deputy Prime Minister Alexander Novak announced on December 8th that the government plans to establish both administrative and criminal liability for violations related to cryptocurrency mining.

More than 100 illegal mining farms have been identified since the beginning of 2025 in the North Caucasus Federal District, according to the company "Rosseti Severny Kavkaz" (Russian Grids of the North Caucasus) as cited by TASS. The total amount of damage exceeded 656 million rubles.

Cryptocurrency mining is prohibited in the republics of the North Caucasus until the spring of 2031. However, many illegal miners remain in the regions, stealing electricity by bypassing meters or connecting to the grid directly without contracts.

5 underground farms were discovered in Karachay-Cherkessia (damage of 104.1 million rubles), two in the Stavropol Territory (1 million rubles) and North Ossetia (390 thousand rubles), and one more in Kabardino-Balkaria (5.8 million rubles).

The most significant damage to energy companies was caused by 14 farms in Ingushetia — 455.5 million rubles. The largest number of illegal miners was identified in Dagestan. There, 79 farms were found, which caused damage amounting to 89.5 million rubles.

"Dagestan still leads in the number of illegal mining cases. This is related to attempts by certain citizens to profit without using their own funds," the energy company reported.

On December 8, at a meeting of the Council for Strategic Development and National Projects, Deputy Prime Minister Alexander Novak stated that the government plans to establish both administrative and criminal liability for violations related to cryptocurrency mining. Lawyers told "RBC-Crypto" where the line between administrative and criminal offenses in the field of mining might be drawn.

In Belarus, reasons for blocking crypto exchange websites have been named. What is known.

Bitcoin mining difficulty has fallen for the third time in a row. What this indicates.

The Central Bank has proposed limiting ordinary Russians' right to purchase cryptocurrency.

Preguntas relacionadas

QHow many illegal mining farms were discovered in the North Caucasus since the beginning of 2025?

AMore than 100 illegal mining farms were discovered.

QWhat is the total amount of financial damage caused by these illegal mining operations?

AThe total financial damage amounted to more than 656 million rubles.

QUntil when is cryptocurrency mining banned in the republics of the North Caucasus?

ACryptocurrency mining is banned until the spring of 2031.

QWhich Russian region suffered the most financial damage from these illegal mining farms?

AIngushetia suffered the most financial damage, with 14 farms causing 455.5 million rubles in damage.

QWhat did the Russian government plan to establish regarding cryptocurrency mining violations, as stated by Deputy Prime Minister Alexander Novak?

AThe government plans to establish both administrative and criminal liability for violations related to cryptocurrency mining.

Lecturas Relacionadas

Comprehensive Analysis of the AI Inference Market: How Can Crypto Projects Break Through?

"AI Inference Market: A Strategic Overview and Crypto's Path to Disruption" The AI inference market, where trained models generate responses to user prompts, is now the primary economic driver, surpassing model training in value. This market is fragmented: hyperscalers (AWS, Google, Microsoft) dominate enterprise reliability; specialized providers (Together, Fireworks) optimize performance; and routing platforms like OpenRouter act as critical bottlenecks, dynamically allocating requests based on cost, latency, and privacy. Crypto AI networks are not competing directly on reliability but are carving out distinct niches: permissionless access, lower-cost supply, privacy, verifiable computation, and agent-native payments. Key projects include Chutes (decentralized inference platform), Akash & io.net (GPU marketplaces), Targon (confidential computing), Darkbloom & Venice (private, consumer-focused inference), and NuNet (orchestration for distributed workloads). The core differentiator is that traditional providers sell trust and enterprise workflows, while crypto networks offer new incentive loops, censorship resistance, and programmable access to resources like compute. For crypto projects to succeed, key metrics are paid token volume (not just usage), sustainable GPU provider revenue, integration into routers like OpenRouter, robust verification against fraud, and genuine privacy guarantees. Ultimately, market control will belong to entities that route, verify, and settle demand—not just those supplying raw compute. The inference market is evolving to resemble a financial system, with tokens as units of account, and crypto's unique value propositions position it to capture emerging segments in this expanding landscape.

Foresight NewsHace 16 min(s)

Comprehensive Analysis of the AI Inference Market: How Can Crypto Projects Break Through?

Foresight NewsHace 16 min(s)

The War Without a Unified Name: The Domestic Tech Giants' World Model Landscape

The article outlines the diverse and fragmented landscape of "World Models" in China's tech industry, where major players are pursuing similar goals under different names like world foundational models, physical AI, or integrated within autonomous driving and embodied intelligence systems. The core aim is to enable AI to create an internal, dynamic environment for simulation, reasoning, and learning, reducing reliance on infinite real-world data. This "data engine" allows for unlimited generation, experimentation, and iteration. The report categorizes the approaches of different companies: * **Internet Giants:** Alibaba is developing models for linguistic, virtual, and physical worlds (Qwen-AgentWorld, HappyOyster, Qwen-RobotWorld). Tencent's HY-World focuses on 3D, game, and social scenarios. ByteDance leverages its vast video data for a potential "digital twin" model. Huawei integrates its model into industrial applications like smart cars and robotics without separately branding it. Baidu embeds world model capabilities within its Apollo autonomous driving and Ernie systems. * **Automakers:** Companies like NIO, Li Auto, XPeng, and Geely are using world models as virtual "driving schools" and "testing grounds." They generate complex scenarios (e.g., rain, snow) to train and validate autonomous driving systems in simulation, aiming for more capable and safer AI drivers. * **Autonomous Driving Suppliers:** Firms such as Momenta, Horizon Robotics, Haomo.ai, and DeepRoute.ai are building the underlying "world engines." They focus on large-scale video generation for simulation, reinforcement learning, and enhancing end-to-end autonomous driving models, often integrating these capabilities into commercial products. While startups bring focus and innovation, they face challenges like limited data, compute resources, and deployment channels. Large companies possess these advantages and are rapidly transitioning world models from research projects into core business infrastructure powering products in vehicles, games, and industry. The conclusion is that world models represent an evolution and convergence of existing AI fields into crucial industrial infrastructure, moving the competition from simply building a model to effectively deploying it to understand and interact with the physical world.

marsbitHace 32 min(s)

The War Without a Unified Name: The Domestic Tech Giants' World Model Landscape

marsbitHace 32 min(s)

Trading

Spot
Futuros
活动图片