Contents

This report was prepared by GINC in mid-2025 to provide a comprehensive analysis of Russia's national AI strategy, drawing on the latest policy developments, regulatory frameworks, and global positioning. The table of contents below outlines the structure of the report, organized into five thematic parts covering strategic vision, innovation systems, sectoral deployment, social impact and policy evolution over time.

Part 1: National Vision and Strategic Foundations
1A. Strategic Vision & Objectives
1B. Governance Architecture
1C. Policy Instruments & Incentives

Part 2: Innovation System and Talent Development
2A. R&D and Innovation Ecosystem
2B. Talent, Education & Mobility
2C. Data, Compute & Digital Infrastructure

Part 3: Sectoral Integration and Global Influence
3A. Industrial Deployment & Tech Diffusion
3B. Regulatory, Ethical & Safety Frameworks
3C. International Engagement & Standards
3D. Defence, Security & Dual-Use Considerations

Part 4: Performance, Resilience and Social Impact
4A. Performance Metrics & Monitoring
4B. Strategic Foresight & Resilience
4C. Foundational AI Capabilities
4D. Public Trust, Inclusion & Social Equity

Part 5: Evolution of Russia's National AI Strategy (2015–2025)

Part 1: National Vision and Strategic Foundations

Russia’s National AI Strategy is driven by a desire to secure technological sovereignty, bolster economic modernization, and enhance national security through artificial intelligence. Initially adopted in 2019 and updated in 2023, the strategy lays out an ambitious vision to position Russia as a leading AI power by 2030. It frames AI as both a tool for domestic development and a domain of geopolitical competition, prompting strong government support for AI research, talent cultivation, and industrial adoption. The foundation of this vision is a state-led approach that integrates public and private efforts, guided by principles of sovereignty, security, and ethical use of AI technologies . In this section, we examine Russia’s strategic objectives, the governance structures established to implement them, and the policy instruments mobilized to advance the national AI agenda.

1A. Strategic Vision & Objectives

Russia’s leadership views AI as a strategic frontier essential for economic strength and national power. The National AI Strategy – authorized by President Vladimir Putin’s decree – defines broad goals to improve citizens’ quality of life, ensure national security, and boost the competitiveness of the Russian economy . It explicitly aspires for Russia to attain “leading positions [in AI] the world over” by 2030 . This section explores how Russia frames AI in geopolitical terms, outlines its national objectives and timelines, and emphasizes technological self-reliance and values in AI development.

AI as a Geopolitical and Economic Priority

Russian officials frequently characterize AI as a critical arena of global competition that Russia must not fall behind in. President Putin famously remarked in 2017 that “whoever becomes the leader in [AI] will become the ruler of the world” , underlining the high stakes Russia attaches to AI leadership. In strategic documents and speeches, AI is likened to a new “arms race” in technology, pivotal for future military and economic power.

The 2019 National AI Strategy casts AI as a pillar of 21st-century national strength, on par with nuclear or space capabilities, and declares it indispensable for sovereignty in the digital age . This framing has made AI development a top national priority: Moscow sees AI as key to driving GDP growth, modernizing industries, and projecting influence, especially as great power rivals invest heavily in AI. By elevating AI to a geopolitical priority, the strategy justifies significant state intervention and investment to ensure Russia can compete with leading AI nations in both civilian and military realms .

National Goals and 2030 Leadership Ambitions

Russia’s AI strategy sets specific milestones on the path to global competitiveness. Two defining target years are 2024 and 2030: by 2024, Russia aimed to “significantly improve its positions” in AI, and by 2030, to “eliminate its lag behind developed countries” and achieve “leadership in certain AI-related areas” . This implies a realistic focus on excelling in select niches rather than overtaking global leaders across the board. The strategy’s goals encompass broad socio-economic outcomes. It seeks to harness AI to enhance public welfare and service delivery, from smarter healthcare and education to more efficient transportation and agriculture. For example, AI is seen as a means to improve decision-making, automate routine processes, and boost productivity in both industry and government .

By 2030, tangible growth is expected in the domestic AI ecosystem: the annual value of AI-related services is projected to reach at least 60 billion rubles (up from 12 billion in 2022) . Likewise, Russia plans to dramatically expand its AI talent pool, increasing the number of annual AI-specialized university graduates from about 3,000 to 15,500 . These targets reflect an overarching objective of the strategy – to secure a sustainable, homegrown AI sector that can drive economic growth and reduce reliance on foreign technologies. Notably, the strategy envisions Russia becoming one of the world’s premier AI innovation centers by 2030, aligning with President Putin’s directive that Russia must be among the leaders of the AI “race” .

Technological Sovereignty and Value Framework

Embedded in Russia’s AI vision is a strong emphasis on technological sovereignty and the alignment of AI development with national values. The strategy was updated in 2023 with explicit recognition of new challenges posed by “unilateral restrictive measures” (international sanctions) and foreign tech bans, which Russia terms “unfair competition” . In response, the strategic focus has shifted even more toward self-reliance: Russia prioritizes building its own AI hardware, software, and data resources so that critical sectors are not dependent on external providers . This includes developing domestic cloud infrastructure and supercomputing capacity, as well as indigenous AI algorithms (for example, Russian-language NLP and computer vision tuned to local needs).

Sovereignty in AI is framed as essential for national security – ensuring that the country’s AI systems cannot be crippled by foreign restrictions . At the same time, the strategy outlines principles to guide AI use in accordance with Russian legal and ethical norms. It emphasizes protections for human rights and privacy, the importance of security and transparency of AI systems, and the need for “technological integrity” throughout a system’s lifecycle . In 2021, the government introduced an ethical AI framework promoting fairness, transparency, and “data sovereignty,” aiming to prevent misuse of AI and align applications with societal values . The updated strategy also stresses explainability of AI algorithms and open availability of information about how they work , reflecting a concern for public trust.

Indeed, Russia explicitly tracks citizen confidence in AI, aiming to raise the public “level of trust in AI technologies” from 55% in 2022 to at least 80% by 2030 . In sum, Russia’s vision is not just to obtain advanced AI, but to do so on its own terms – maintaining control over the technology, imbuing it with national values (like stability and rule of law), and ensuring it serves Russian interests in an era of strategic competition.

1B. Governance Architecture

Implementing Russia’s AI ambitions requires a centralized yet collaborative governance structure. The National AI Strategy is overseen at the highest levels of government, signaled by the fact that it was established by Presidential decree and personally updated by President Putin . This top-down authority provides strategic direction, while execution is distributed across ministries, state corporations, and research institutions.

The government has set up formal mechanisms to coordinate AI policy, often leveraging existing national programs and the influence of major state-backed companies. A notable feature of Russia’s AI governance is the prominent role of large domestic tech firms (many state-owned or affiliated) in both advising on policy and carrying out implementation . This section examines the key organizational components of Russia’s AI governance: the central leadership and advisory bodies, the public-private alliance driving AI, and the interagency and institutional arrangements enabling policy execution.

Presidential Oversight and Strategic Coordination

Central oversight for AI policy in Russia is exercised by the President and the federal government (Cabinet), ensuring alignment with national strategic goals. The original AI Strategy (2019–2030) was approved by President Putin, and in February 2023 he signed a decree updating it with ~40 pages of amendments . High-level directives from the President set priorities such as emphasizing domestic solutions and responding to geopolitical shifts . Implementation responsibility falls to the Prime Minister and relevant deputy prime ministers: for instance, Deputy PM Dmitry Chernyshenko oversees digital development and has chaired strategic sessions on AI .

In late 2022, the government created a detailed roadmap “AI Development to 2030” to operationalize the strategy, which was approved by the Ministry of Economic Development and then endorsed by Chernyshenko in early 2023 . This roadmap breaks down tasks, timelines, and funding across agencies and partners. It also integrates AI efforts into Russia’s broader national projects framework. Initially part of the “Digital Economy” national program, AI development is now migrating into a new “Data Economy” national project to reflect its cross-cutting importance . Through the Council for Strategic Development and national project oversight, top officials monitor progress on AI targets. In short, Russia’s governance architecture for AI is anchored by centralized strategic direction (via presidential decrees and national projects) combined with delegated management to ministries and state bodies for day-to-day execution.

Industry Alliance and State-Owned Champions

A hallmark of Russia’s AI governance is the Alliance for Artificial Intelligence, a public-private consortium that bridges government with leading technology companies. Formed in November 2019 at the AI Journey conference (with Putin in attendance), this AI Alliance includes major firms like Sberbank (Sber), Yandex, Mail.ru Group (now VK), Gazprom Neft, MTS, and the Russian Direct Investment Fund .

The Alliance’s mandate is to coordinate AI efforts between business and the state and to advocate for policies conducive to AI innovation. It has been tasked with lobbying for streamlined regulations (for example, easing rules for deploying driverless vehicles and using data) and accelerating AI adoption in priority areas . The Alliance effectively serves as an extension of the governance apparatus by bringing industry expertise into policy design and implementation. Its Supervisory Board is chaired by a top Sberbank executive, and it works closely with government agencies on setting standards and identifying project opportunities .

In practice, Russia relies on a handful of large state-aligned “champions” to drive its AI agenda – notably Sber, which not only spearheaded the drafting of the national strategy but also is the primary developer for many AI solutions . Other state-owned enterprises like Rostec (defense conglomerate) and Rosatom have their own AI programs aligned with national goals, particularly in defense and heavy industry. This model of governance – a close-knit collaboration between the state and big tech/defense companies – ensures that AI initiatives have the necessary scale and funding. However, it also means the AI ecosystem is steered by a few large players. The government’s approach leverages these entities’ resources: for example, Sber’s AI Lab and supercomputing center (Christofari) function as national assets, and Yandex’s advances in autonomous driving or language models feed into state priorities. The AI Alliance formalizes this partnership, making industry an integral part of governance and pooling public-private efforts toward the strategy’s targets .

Interagency Implementation Mechanisms

To implement the AI strategy across diverse sectors, Russia utilizes a network of ministries, agencies, and state institutions each handling elements of the AI agenda. The Ministry of Digital Development, Communications and Mass Media (MinTsifry) plays a central role in regulatory and infrastructure aspects of AI, overseeing digital economy programs and initiatives like the experimental legal regimes for AI. The Ministry of Economic Development coordinates the economic and innovation components, including the AI roadmap, and monitors outcomes like market size and investment . Other ministries lead domain-specific AI adoption – for instance, the Ministry of Health for medical AI solutions, Ministry of Education and Science for talent programs, and Ministry of Defense for military AI research. An interagency working group on AI was established to align these efforts, often reporting to the Prime Minister’s office. Moreover, about 30 organizations (spanning government bodies, top universities, research institutes, and innovation funds) were designated in 2022 as responsible partners in carrying out AI projects under the 2030 roadmap . This included institutions like Moscow State University, the Skolkovo Foundation, and sectoral regulators.

The involvement of academic and scientific organizations (e.g. the Russian Academy of Sciences institutes) is meant to ensure cutting-edge R&D supports the strategy. Meanwhile, standardization and technical guidelines for AI are being developed by Rosstandart and bodies like the AI Alliance’s technical committees. The governance architecture thus resembles a hub-and-spoke model: a central strategy emanating from the Kremlin and Government, with implementation “spokes” in key ministries and partner entities. Regular progress reviews are built into this system. For example, annual reports on the National AI Strategy’s execution have been released (a comprehensive progress report came out in 2022 detailing advances in AI research, smart cities, healthcare, etc. ). This not only tracks achievements but also helps update policies. In 2023, such feedback loops influenced the strategic update to tackle identified gaps in computing capacity, data, and skills . In summary, Russia’s AI governance blends top-level control with distributed implementation, relying on both government hierarchy and alliances with major stakeholders to translate strategic aims into concrete outcomes.

1C. Policy Instruments & Incentives

The Russian government has deployed a variety of policy tools and incentives to drive the development and adoption of AI nationwide. These include direct funding and investment programs, regulatory adjustments to foster innovation, and partnerships to build capacity. The approach is largely state-driven: substantial public resources are being funneled into AI research and infrastructure, while laws and standards are crafted to create a favorable environment for AI technologies (from sandboxes for testing to data regulations). At the same time, authorities use soft instruments like education initiatives and public awareness efforts to ensure a skilled workforce and societal readiness for AI. In this section, we detail the key instruments Russia is leveraging – from funding mechanisms and national projects, to legal frameworks and industry incentives – to realize its AI strategy goals.

Public Investment and National Programs

Government funding serves as a cornerstone of Russia’s AI strategy implementation. In contrast to more market-led models, Russia relies on orchestrated public investment alongside mandated industry co-investment to meet its targets. The National AI Strategy was accompanied by the creation of a dedicated federal AI development program (or “roadmap”), initially under the Digital Economy national project. Although early plans envisioned around 57 billion rubles of federal funding for AI by 2024, the strategy was recalibrated after 2022. The current roadmap projects a total of 24–25 billion rubles in state budget spending on AI from 2023 to 2030 . This funding is directed to priority research areas, startup grants, computing infrastructure, and incentives for AI adoption in industry. In addition, extrabudgetary (private) investment has been pledged: notably, Sberbank alone is expected to invest nearly 100 billion rubles of its own funds into AI solutions by 2030 , dwarfing the state’s contribution. Other large companies (Gazprom Neft, Yandex, MTS, etc.) and venture funds are also contributing capital under government agreements.

The focus of investment is mission-oriented – for example, the 2030 roadmap identifies four key technology domains to advance: natural language processing, computer vision, intelligent decision support systems, and new AI methods (like deep learning innovations) . For each domain, financed projects must progress from research to prototypes to full-scale products (such as Russian-language AI assistants, autonomous vehicle vision systems, etc.). The government has also funded the establishment of AI research centers and upgraded supercomputing facilities (one flagship being Sber’s Christofari AI supercomputer). Importantly, public procurement is used as a stimulus: state agencies and state-owned enterprises are encouraged or required to pilot domestic AI solutions, guaranteeing a market for Russian AI products. For instance, the healthcare ministry is rolling out AI diagnostics in hospitals across regions, backed by federal funds, to meet the target of at least 10 AI solutions in healthcare per region by 2030 . In sum, Russia’s financing strategy for AI relies on strategic public spending to leverage much larger industry investments, guided by national priorities. This approach, while constrained by budget realities and sanctions (which have reduced available funds ), seeks to concentrate resources on areas with high impact and to ensure that funding translates into operational AI deployments in the economy.

Regulatory Sandboxes and Legal Frameworks

Russian policymakers have introduced a series of legal and regulatory measures to accelerate AI innovation while managing its risks. One key instrument is the use of “experimental legal regimes,” essentially regulatory sandboxes, that allow companies to pilot AI technologies with temporary exemptions from standard regulations. In 2020, President Putin signed a law “On Experimental Legal Regimes in Digital Innovation” enabling such sandboxes . The first major application was an AI sandbox in Moscow, launched July 2020 for a five-year term, to test AI solutions (like computer vision in urban services) under relaxed rules in a controlled environment. This experiment aims to identify what regulations (data use, liability, etc.) need adjustment to facilitate AI deployment . Since then, additional sandbox initiatives have extended to areas such as autonomous vehicles and telemedicine AI, providing a safe legal space for innovators. The government is also actively updating laws to address AI. In mid-2024, for example, the State Duma passed legislation on the civil liability for harm caused by AI systems, ensuring that there is a clear insurance and compensation mechanism as AI algorithms take on more decision-making roles.

Data governance has been tightened to support AI: existing personal data law was amended to clarify principles of consent and anonymization for AI training purposes , and officials stress the “high level of personal data protection” needed during AI model development . At the same time, to improve access to data for AI developers, the government has promoted open data portals and encouraged businesses to share non-personal datasets, albeit within sovereign controls.

Standards and certification are another facet – Russia is developing its own GOST standards for AI software quality, and the strategy calls for involvement in international standard-setting to ensure Russian AI products can be certified globally . Notably, Russia has taken a permissive stance on certain controversial AI uses: it opposes blanket bans on technologies like autonomous weapons, instead favoring international “rules of the road” that would still allow military AI development . Domestically, however, ethical guidelines were introduced. A national Code of Ethics for AI was adopted (on a voluntary basis) in 2021 by major companies and the government, outlining principles such as non-discrimination, transparency, and human oversight in AI decisions. Many of these principles have been integrated into the updated strategy, which explicitly mentions “transparency and explainability of algorithms” as guiding norms . In essence, Russia’s regulatory strategy tries to strike a balance: lowering barriers to experimentation and deployment for local AI innovations, while establishing legal norms to build trust and accountability. The use of flexible, experimental regimes and iterative lawmaking reflects an understanding that regulations must evolve alongside the technology – a recognition shared by many countries, but executed in Russia’s case through strong central guidance and bespoke legal tools tailored for AI.

Industry Collaboration and Capacity-Building Incentives

To cultivate a robust AI ecosystem, Russian authorities have implemented policies that incentivize collaboration and build domestic capacity in both industry and academia. The Alliance for AI, as discussed, is a prime vehicle for public-private collaboration: it not only advises on policy but also orchestrates joint projects among its member companies and with government support. For example, the Alliance members collectively work on creating open-source AI libraries in Russian, developing shared datasets, and solving applied challenges in key sectors (energy, agriculture, etc.), with government funding matches or tax benefits as encouragement . The state provides incentives such as preferential procurement for certified “Made in Russia” AI solutions and has designated AI as a strategic industry eligible for reduced taxes and soft loans (building on broader IT sector incentives enacted in recent years).

Research and innovation incentives are also in place: the Russian Science Foundation and Ministry of Science run grant competitions for AI R&D, offering universities and startups funding for promising projects in priority areas. Skolkovo, Russia’s innovation hub, houses numerous AI startups and provides them tax breaks and mentorship under its special economic zone status. Similarly, the Skolkovo Institute of Science and Technology and other leading universities received grants to establish AI centers of excellence and incubators.

A significant thrust of capacity-building is in human capital development for AI. Recognizing the talent shortage, the strategy introduced measures to expand AI education and retain skilled professionals. By government mandate, AI-related curricula and degree programs have been rolled out “at all educational levels” – from new specialized Master’s programs in machine learning to basic coding and AI modules in secondary schools . The Ministry of Education launched an “AI University Alliance” to connect academia with tech companies, ensuring graduates have practical skills. Scholarships and increased university admissions in AI fields have been funded to meet the ambitious target of 15,000+ AI graduates annually by 2030 . For the existing workforce, the government sponsors large-scale upskilling programs, including online AI courses and professional retraining initiatives, to diffuse AI know-how across industries. To counteract brain drain, incentives are offered to young scientists and engineers to stay in or return to Russia – for instance, sizable grants for AI researchers who relocate to Russian labs, and support for international collaborations that allow Russian talent to work with global peers while based at home. The strategy also makes a point of “recruiting foreign specialists” in AI where needed , simplifying visa and immigration rules for tech experts to attract talent from abroad, especially from neighboring countries.

Finally, the government sponsors events and challenges to spur AI solutions for national priorities. Annual forums like AI Journey (hosted by Sber with government backing) and competitions such as the Russian AI Cup create enthusiasm and connect innovators with investors. Several “grand challenges” in AI (e.g. a competition to improve Russian speech recognition, or AI for Arctic logistics) have been launched with prize funding, both to solve problems and to signal political support for AI. These softer incentives complement the funding and regulatory tools, aiming to build an innovation culture around AI. The combined effect of these policies – from alliance-driven collaboration to education and contests – is to foster a self-sustaining AI ecosystem. The Russian state’s role is unmistakably proactive: it acts as catalyst, convenor, and patron of AI development, using every lever at its disposal to mobilize the country’s scientific and industrial capacities in pursuit of the National AI Strategy’s objectives .

Part 2: Innovation System and Talent Development

2A. R&D and Innovation Ecosystem

Strategic R&D Priorities and National Programs

Russia’s National Strategy for AI (2019) set ambitious goals to make the country a world leader in AI by 2030, emphasizing technological self-sufficiency and leadership in key sectors . The strategy outlined milestones for 2024 and 2030, aiming to expand the AI innovation base by 50% and build a high-tech, export-oriented AI industry in areas like manufacturing and agriculture . To implement these goals, the government launched a dedicated federal project “Artificial Intelligence” under the national Digital Economy program, funding AI research and infrastructure. During 2021–2024, roughly ₽31.5 billion (including ₽27.4 billion federal funds) was allocated to this AI project . In 2025, the program’s budget was raised to ₽7.7 billion, underscoring AI as a strategic priority . These resources support a range of measures – from R&D grants to building data platforms – and are complemented by presidential decrees and government orders to adjust regulations and spur innovation . Notably, President Putin updated the AI Strategy in early 2023 with ~40 pages of amendments addressing new challenges, such as insufficient computing capacity, lack of domestic AI solutions, and shortage of skilled personnel, exacerbated by foreign sanctions . This update reaffirmed federal support for scientific research and talent development in AI, despite geopolitical headwinds.

Public-Private Alliances and Industry Leadership

A hallmark of Russia’s AI ecosystem is the strong role of major state-affiliated companies and their alliances. In late 2019 – shortly after the national strategy’s adoption – the AI Alliance Russia was formed by five of the country’s largest corporations (Sberbank, Yandex, Mail.ru Group, MTS, and Gazprom Neft) together with the Russian Direct Investment Fund . This alliance is a public-private partnership meant to “coordinate the efforts of the business and scientific communities” toward national AI objectives . In practice, it has become a driving force for AI development: rather than a startup-driven model, Russia’s AI innovation is largely led by these big tech players and state-owned enterprises . The AI Alliance has launched initiatives like AI-Hub, an investment platform linking AI startups with corporate accelerators and investors, including foreign participants . It also spearheads data-sharing efforts – for example, creating an open library of Russian and international datasets to accelerate AI product development . Alliance members provide mentorship in schools and have equipped 500 schools with AI learning simulators , reflecting a broad mandate from talent pipeline to venture support. These collaborations leverage the strengths of each member: for instance, Sberbank (now “Sber”) contributes its financial heft and AI R&D labs, Yandex brings search/data expertise and large cloud infrastructure, and Gazprom Neft and MTS integrate AI in energy and telecom domains. By aligning industry leaders, Russia has fostered a decentralized yet coordinated innovation ecosystem not unlike the US model , but with heavier state influence. A clear example is the joint focus on priority research areas – e.g. autonomous vehicles, natural language processing (NLP), and facial recognition – where companies like Yandex, Sber, and Rostec (the state defense conglomerate) collaborate with research institutes . This alliance-centric model has yielded tangible outputs: new AI products, shared open-source tools, and a unified voice lobbying for pro-AI policies (such as streamlined regulations for driverless cars and data usage) . It demonstrates how Russia’s innovation ecosystem blends government direction with corporate execution, ensuring that even as startups play a smaller role, cutting-edge AI development continues within well-resourced institutions.

Research Centers and Breakthrough Innovation Programs

To bolster foundational research and “breakthrough” innovation, the Russian government has invested in a network of AI research centers across top universities and institutes. Starting in 2021, competitive grants have been awarded in multiple waves to create specialized R&D centers focused on topics like trustworthy AI, healthcare AI, and smart cities. In 2023, a third wave of these centers was launched: seven winning institutions – including HSE University, Innopolis University, the Russian Academy of Sciences’ ISP RAS, ITMO University, MIPT, Skoltech, and (for the first time) Moscow State University – will each receive ₽676 million over two years (2024–2025), totaling ₽4.7 billion in funding . These centers are tasked with conducting fundamental AI research (e.g. on strong AI and multi-agent systems) and developing “world-class, breakthrough solutions” in sectoral applications . Earlier waves established labs for AI ethics, as well as domain-specific research in medicine, transportation, industry, and urban analytics . According to the Deputy Prime Minister, the first two waves of centers now produce nearly half of all Russian scientific output in AI, validating the government’s investment . The strategy sets an explicit target of 450 top-tier (A) AI conference publications by 2030*, pushing these centers to elevate Russia’s global research footprint . The Ministry of Economic Development and Ministry of Science are also preparing a unified national AI research program to align academic efforts with these priority areas . Beyond academia, state agencies fund industry-specific R&D projects – especially in defense and security, where Russia traditionally excels. For example, the Ministry of Defense has its own AI roadmap, and by 2020 it helped form a “Russian AI Alliance” in the defense sector to drive projects in military robotics, autonomous systems, and cybersecurity . Indeed, defense-related investment in AI (through Rostec and others) ramped up mid-2010s, targeting autonomous weapons, drones, and strategic decision-support systems . Overall, the R&D ecosystem is supported by substantial government financing by Russian standards – even as total spending trails the U.S. and China, Russia has prioritized AI in its federal budget. The updated strategy (2023) allocates targets like reaching an annual AI services market of ₽60 billion by 2030 (up from ₽12 billion in 2022) , signaling continued public investment. Crucially, Russia’s leadership acknowledges that innovation requires a supportive regulatory environment: thus, parallel efforts are underway to remove legal barriers (e.g. sandbox regimes for unmanned vehicles, updated data laws) to speed up the deployment of AI-driven solutions .

Startup Support and Innovation Funding

Historically, Russia’s venture capital market in AI has been modest, posing a challenge for startup-driven innovation . To address this, the state and allied institutions have introduced programs to nurture startups and research commercialization. The Foundation for Assistance to Small Innovative Enterprises (FASIE), a government innovation fund, launched a “Start AI” grant competition to seed early-stage AI projects. Winners can receive up to ₽5 million each, enabling teams to develop prototypes and business plans. According to FASIE, this program has been effective: ₽1.7 billion in government grants yielded ₽4.5 billion in revenue among recipient companies, which collectively created about 700 new jobs . This indicates a healthy return in building a pipeline of AI startups, especially in applied fields like computer vision, predictive analytics, and AI-driven services. In 2024, a new federal initiative titled “Domestic Solutions” was launched to further stimulate AI startups and solution providers across various sectors . Through this program, startups in industries ranging from manufacturing to education and medicine can apply for support, reflecting a broad-based approach to AI innovation diffusion. The AI Alliance’s aforementioned AI-Hub also plays a role by connecting these startups with corporate venture arms and accelerators , ensuring that promising ideas can find mentorship and scale-up opportunities inside Russia’s big tech ecosystem. Additionally, Russia’s sovereign wealth entities and large corporations have begun to invest directly in AI ventures; for example, Sber’s corporate fund and RDIF have backed AI firms in fintech, and state telecom MTS runs an AI accelerator for promising projects. While challenges remain – for instance, Western sanctions have limited access to foreign capital and certain technologies – the government’s strategy clearly views fostering startups and SME innovation as critical to diversifying the AI landscape beyond the incumbents . The creation of special economic zones and tech parks (e.g. at Skolkovo and Innopolis) with tax incentives and grants for AI companies is another lever to encourage entrepreneurship. An emerging result of these efforts is a growing cohort of AI firms in Russia’s private sector: by 2022, approximately 1,000 Russian companies were actively working on AI solutions, with the financial sector leading in adoption . Going forward, Russia’s innovation ecosystem seeks to balance its top-down, big-player-driven model with a more vibrant bottom-up innovation culture – leveraging public funding and alliances to ensure even startups contribute to the national AI agenda.

2B. Talent, Education & Mobility

Expansion of AI Education in Universities

Building a robust talent pipeline is a cornerstone of Russia’s AI strategy. The government has dramatically expanded AI-related programs in higher education to train the next generation of specialists. Between 2021 and 2024, over 120 new educational programs in artificial intelligence were developed across 16 leading universities, supported by ₽600 million in federal grants . As a result, nearly 15,000 students are currently enrolled in these AI-specialized bachelor’s and master’s programs (including about 10,000 master’s students) – a significant increase in capacity compared to the mid-2010s. Top universities such as Moscow State University (MSU), St. Petersburg’s ITMO University, Moscow Institute of Physics and Technology (MIPT), Skoltech, and Higher School of Economics (HSE) have launched dedicated AI faculties or degree tracks, often in partnership with industry. For example, ITMO partnered with Yandex and Sberbank on an AI curriculum (AI360) blending machine learning with practical internships . In early 2025, the government’s Analytical Center opened an Educational Program Expertise Center that selected 22 universities to train AI specialists (and 26 universities for broader IT specialties) as part of a concerted effort to produce “world-class specialists” in AI . This initiative coordinates curriculum modernization, aligning university courses with cutting-edge research and industry needs. The Ministry of Science and Higher Education reports that these efforts have already boosted both the quality and volume of AI education nationwide . To ensure teaching quality, over 5,000 faculty members were retrained in AI-related disciplines in 2024 alone . The National AI Strategy explicitly called for introducing AI modules “at all education levels” and creating professional retraining programs . By infusing AI into university engineering, computer science, and even humanities programs, Russia aims to alleviate its talent shortage. Quantitatively, the strategy’s targets are bold: the annual number of AI graduates is set to grow from roughly 3,000 in 2022 to 15,500 by 2030 . Moreover, Russian universities are striving to attain “world-class” status in AI education; degrees are being designed not only to feed domestic industry, but to be recognized internationally, helping Russia compete in global talent markets. This massive scale-up has started yielding results – Russia consistently produces strong showings in programming and math competitions, and 16 Russian universities (led by MSU and ITMO) were listed among the world’s top computer science institutions in recent rankings . The continued challenge is retaining these graduates in the local AI sector – a challenge the government hopes to meet by growing domestic opportunities and prestige in AI research.

STEM Curriculum Integration and Youth Initiatives

Russia has also moved to integrate AI and coding into K–12 education to cultivate talent from an early age. Pilot programs have introduced basic AI concepts, robotics, and programming in secondary schools, often starting as extracurricular or elective courses. Notably, Sber and the AI Alliance launched a nationwide effort in 2021 to equip 500 schools with AI training simulators and course modules . These tools allow students to learn machine learning basics, work with simple datasets, and even train elementary models in a sandboxed environment. Such early exposure is meant to inspire more pupils to pursue STEM and AI careers (echoing China’s strategy of AI education in schools). The government’s “Sirius” education center (a talent incubator for gifted students in Sochi) has hosted intensive AI bootcamps for high schoolers , and specialized lyceums in cities like Moscow and Kazan now offer AI-focused tracks. In addition, Russia is “popularizing” AI among youth through competitions and contests. A flagship example is the AI International Junior Contest, launched in early 2021, which drew 10,000 participants from 50 countries in its first year . This contest features challenges in areas like Earth observation, healthcare, finance, and robotics – encouraging teenagers to apply AI to real-world problems and “engage and support children with outstanding AI talents worldwide” . Domestically, there are also annual Olympiads in informatics and AI, whose winners often receive direct admission to top universities or are invited to join research labs. To strengthen the teacher pipeline for STEM, the Ministry of Education implemented programs to retrain science teachers in modern programming and data science techniques , ensuring that foundational skills are taught in schools. All these efforts reflect a broader strategy: normalize AI as a basic skill (comparable to foreign languages or math) for the next generation. As the AI Strategy emphasizes, improving math and science education – and integrating it with humanities to develop ethical and well-rounded technologists – is critical . Early results are promising: Russian teens have won international AI and robotics contests, and enrollment in university STEM programs is rising. However, officials note that many regions lag behind the major cities in AI education resources . The new AI Development Center at the Government’s Analytical Center is tasked with “scaling the best regional practices to the whole country” , meaning successful school AI programs from Moscow or Tatarstan should be replicated in other oblasts. By seeding interest and proficiency in AI early, Russia hopes to cultivate a large, home-grown talent base less reliant on importing expertise.

Workforce Upskilling and Retention

Beyond formal education, Russia recognizes the importance of upskilling its existing workforce to adapt to AI-driven transformation. Over the past five years, the number of IT specialists in the country (including those in AI fields) grew by 50% to reach 1.08 million in 2023 . This growth has been aided by government and industry programs for continuous learning. For instance, the Ministry of Digital Development (MinTsifry) launched “TOP-AI” in 2023 – a training initiative targeting students and mid-career professionals with modern AI curricula and certifications. TOP-AI aims to train 10,000 AI specialists by 2030 in advanced topics like neural network architecture, computer vision, and data engineering . Similarly, online education platforms (often backed by state or Sber) offer free or subsidized courses in data science and machine learning, enabling engineers in traditional industries to re-skill for AI roles. Corporate giants also run in-house training: Yandex’s School of Data Analysis and SberUniversity have produced thousands of AI-qualified engineers over the years. The national strategy calls for creating “job development and professional retraining programs” in AI for working adults . In line with this, the government in 2021 set up short-term training grants for public sector employees to learn AI applications, ensuring civil servants and state enterprise staff can implement AI solutions. One notable effort is retraining teachers (mentioned above) and also medical professionals – e.g. courses for radiologists on AI diagnostic tools. The upskilling drive is also motivated by a need to mitigate job displacement; by proactively training workers, Russia hopes to fill new AI jobs internally and smooth the transition in industries undergoing automation. Importantly, the updated strategy of 2023 directly acknowledged the “lack of skilled personnel” in AI as a critical bottleneck . High-level targets were set to increase the talent pool dramatically (for example, quintupling annual AI graduates by 2030, as noted). Additionally, authorities are developing incentives to retain talent, such as higher salaries for AI researchers under the national “Digital Economy” program and funded PhD spots in AI. According to one projection, the total AI workforce in Russia could reach 463,000 by 2035, up from roughly 48,000 in 2023 – though achieving this ten-fold increase will depend on successful retention of graduates and attracting experts from abroad.

Brain Drain and Talent Mobility

Even as Russia expands domestic training, it faces the challenge of brain drain in the tech sector. Political and economic turbulence since 2022 has led to significant emigration of professionals – estimates range from hundreds of thousands up to over a million Russians leaving, many of them highly skilled in IT . This outflow threatens to undermine the talent gains from education reforms. The government has responded by emphasizing “technological sovereignty” and creating local opportunities to dissuade talent from departing. For example, the wave of new AI research centers (with generous funding and prestige) is partly aimed at keeping top scientists in Russia by giving them world-class labs and resources . Additionally, Russia has made it easier for foreign AI specialists to work in the country – offering fast-track visas and tax incentives – although the appeal is currently limited due to geopolitical issues. Interestingly, the 2019 strategy explicitly mentioned recruiting foreign specialists and leveraging international cooperation to build capacity . In practice, before 2022, Russia did attract some talent through programs like “IT Specialist Residence” permits and collaborations with Western companies’ AI labs in Moscow. Moreover, many Russian AI researchers trained abroad (in the US or Europe) have returned over the years to take up roles at Skoltech, Yandex, or Huawei’s Moscow lab. This brain circulation continues, but at a slower rate recently. To shore up local expertise, the government is also engaging the Russian tech diaspora through conferences (e.g., the annual Artificial Intelligence Journey conference hosted by Sber attracts global Russian-speaking AI experts) and joint projects. Another facet of talent mobility is internal: the government encourages experts to work on public sector or defense AI projects by offering competitive salaries and benefits (a notable example is the Ministry of Defense establishing a dedicated AI development unit and recruiting young engineers into “digital troops” roles ). Despite these efforts, officials candidly note that “unilateral restrictive measures” by other countries have created hurdles – i.e., sanctions not only restrict hardware but also incentivize some experts to move abroad . Therefore, a key focus of the updated strategy is building a self-sustaining talent ecosystem: one that can produce, attract, and retain AI specialists under any external conditions. The introduction of an AI Ethics Code in 2021, signed by over 400 organizations, can also be seen as a measure to build public trust and a stable environment so that AI professionals feel their work aligns with societal values . In summary, Russia’s talent strategy is two-pronged – grow talent at home and reduce brain drain – to ensure that human capital does not become the Achilles’ heel of its AI ambitions.

2C. Data, Compute & Digital Infrastructure

Data Governance and Sovereignty

In Russia’s AI strategy, data is viewed as a strategic national resource and a critical enabler for AI development. The government has steadily moved to strengthen data governance frameworks that ensure both the availability of large datasets for AI training and the sovereignty of that data (i.e. control by Russian entities). Since 2015, laws have required personal data on Russian citizens to be stored on servers in Russia – an early step toward data localization. The 2019 AI Strategy went further, calling for improvements in data access and quality for developers, including the creation of open data sets and data libraries for AI . In 2021, the authorities adopted a Code of Ethics for AI, a voluntary code that over 400 organizations have signed, which encourages responsible handling of data (transparency, privacy protection) in AI systems . This “soft regulation” aims to build public trust so that more data can be harnessed for innovation without violating rights. As of 2023, Russia has also been consolidating public datasets into integrated platforms: for example, a unified biometric database (for banking security) and nationwide databases in healthcare and agriculture to support AI solutions in those fields . Experts have highlighted the need for a national data lake infrastructure – large, quality-controlled repositories of annotated data that researchers can use . Indeed, a proposal in 2025 is to establish an AI Development Center that would act as an operator of national “data lakes” and open anonymized datasets, serving as a secure exchange platform between businesses, academia, and government . This would address a current pain point: the shortage of high-quality, labeled data in domains like medicine, government services, and science . On the commercial side, the AI Alliance members have begun pooling certain data or creating data marketplaces – for instance, Sberbank opened some anonymized banking datasets for fintech AI contests, and the city of Moscow’s open data portal shares transportation and urban data that startups use for AI models. Additionally, Russia’s legal environment is adapting: the Data Protection Law and Digital Rights Act (both updated in 2022) provide guidelines on data anonymization and usage, and an upcoming law may allow controlled data sharing between companies for AI training under a regulatory “sandbox.” The strategic intent is clear: Russia wants to minimize reliance on foreign data or platforms (especially given that much AI training data globally comes from Western platforms). By asserting data sovereignty, Russia hopes to shield its AI sector from external disruptions and ensure compliance with its own norms (for example, ensuring training data does not conflict with national values or security). However, a balance is needed – too strict data localization could deprive Russian AI researchers of valuable global datasets. The government’s approach so far seeks a middle ground, leveraging domestic big data (e.g. millions of CCTV images, satellite data from Roscosmos, extensive public health records) while selectively participating in international data collaborations in areas like climate change and pandemic response. Notably, Russia is investing in data labeling and quality improvements: initiatives are underway to use crowdsourcing and even AI tools themselves to label data in Russian language and contexts, so that local AI models (e.g. for Russian NLP) can be trained on world-class datasets. Overall, data infrastructure in Russia is being nationalized and expanded simultaneously – a foundational effort to fuel AI algorithms with the “new oil” of the digital economy.

Computing Capacity and High-Performance Infrastructure

Access to powerful computing resources – from cloud GPUs to supercomputers – is essential for modern AI (especially training large models), and Russia’s strategy acknowledges this as a critical gap. In fact, the 2023 strategy update explicitly cited the “lack of data processing capacities” as a major challenge in the wake of new market conditions . To address this, Russia has been scaling up its high-performance computing (HPC) infrastructure in recent years, primarily through investments by leading tech companies and state research centers. In 2019, Sberbank unveiled Christofari, the first supercomputer in Russia designed specifically for AI workloads . Christofari (named after Sber’s first client) delivered 6.7 petaflops and immediately ranked among Russia’s top supercomputers. By 2021, Sber launched Christofari Neo, a second-generation AI supercomputer boasting over 700 Nvidia A100 GPUs and nearly 12 petaflops of performance . Christofari Neo was briefly the most powerful system in Russia until Yandex built an even larger cluster: in late 2021 Yandex’s new supercomputer (nicknamed “Chervonenkis”) achieved 21.5 petaflops, placing it in the global top 20 (19th on the Top500 list) . Along with two slightly smaller Yandex supercomputers (“Galushkin” and “Lyapunov”), these systems give Russia a cadre of AI-focused supercomputers that are used for training advanced models (such as Yandex’s large language models and Sber’s GigaChat GPT-like model). As of 2021, Russia had at least 4 systems in the Top500 supercomputer ranking, up from just 1 or 2 a few years prior . Nevertheless, this is still far behind the U.S. and China – in 2020 Russia had only 3 of the world’s top 500 supercomputers, compared to 228 for China and 117 for the U.S. . This disparity underscores the ongoing challenge in compute capacity. Moreover, since 2022, Western export controls on high-end semiconductors (GPUs like Nvidia A100/H100) have tightened, making it harder for Russia to procure cutting-edge AI hardware . To counter this, the Russian government and industry are exploring domestic chip development: for instance, the Ministry of Industry and Trade has supported research into neuromorphic processors and AI accelerators that mimic brain functions . Some Russian companies (like MCST and Baikal Electronics) have designed indigenous CPUs and are evaluating AI-friendly adaptations, but these efforts are in nascent stages and lag global leaders by several generations. In the interim, Russia is reportedly sourcing more hardware through alternative channels and leaning on existing capacity. The national supercomputing network is also being expanded geographically – new data centers with HPC capability are being built outside Moscow, such as Sber’s 55,000 m² data center in Saratov region announced in 2021 . There is a strategic push to use energy-abundant regions (for example, Siberia) for future compute clusters, akin to how China distributes its computing (“Eastern Data, Western Compute”). By mid-2024, a Russian tech blog estimated the country’s total AI compute at around 20–25 petaflops in FP64 terms (for comparison, the U.S. is far ahead), highlighting room for growth. The government’s goal is to ensure that researchers and companies inside Russia can train sophisticated models at home without needing foreign cloud services. In summary, Russia’s AI compute infrastructure is improving through corporate initiatives (Sber, Yandex, MTS have all built notable supercomputers ) and state support, but it remains a pressure point. The strategy calls for continued investment in HPC centers and even quantum computing R&D, as well as promoting efficient use of compute (via cloud sharing and scheduling) to maximize what is available . This is crucial for achieving AI leadership, as computing power often correlates with the ability to develop advanced AI models.

Domestic Cloud and Digital Platforms

Parallel to raw compute power, Russia is developing a robust digital infrastructure and cloud ecosystem to support AI deployment at scale. Rather than relying on AWS, Azure, or Google Cloud (which are restricted or exited from the market), Russian companies have built indigenous cloud platforms. SberCloud, launched by Sber in 2018, provides cloud and AI-as-a-service offerings out of data centers on Russian soil . SberCloud integrated the Christofari supercomputers into its services, allowing businesses and researchers to rent GPU time on the country’s top AI machines for training models . Similarly, Yandex Cloud has become a key player, offering AI development toolkits, large-scale storage (Yandex’s open-sourced YTsaurus big data platform is analogous to Hadoop/HDFS ), and on-demand compute powered by Yandex’s own data centers. After 2022, VK Cloud (Mail.ru) also expanded to capture organizations shifting off Western clouds. The existence of these domestic cloud providers ensures that Russian enterprises and government agencies can access scalable computing and data services while keeping data and processes within national jurisdiction – a big advantage for data sovereignty and security. Moreover, they provide pre-trained models, API services (for speech recognition, computer vision, etc.), and development sandboxes that lower the barrier to AI adoption for smaller firms. Another pillar of digital infrastructure is software frameworks. To reduce dependency on foreign AI software, Russian tech firms are investing in open-source frameworks and platforms. For example, Sber has open-sourced several tools (like its AutoML library and NLP models), and as mentioned, Yandex open-sourced YTsaurus in 2023 . There is also an effort to promote Russian-made operating systems (like Astra Linux) and cloud stacks in public procurement, to foster a self-reliant tech stack for AI. In 2025, the government announced plans to create a “unified digital platform for big data processing” as part of the national AI project . This platform would presumably connect disparate data sources, provide computing resources, and host AI solutions that can be reused across regions (for instance, a computer vision service for smart city traffic management that any city can plug into) . By standardizing such infrastructure, Russia hopes to accelerate AI diffusion outside tech hubs. Another aspect is the push for technological independence in critical AI subsystems – often referred to as achieving “technological sovereignty”. The idea is to replace or localize everything from chips to cloud to applications. As an example, when NVIDIA’s latest GPUs became unavailable due to sanctions, Russian entities started testing Chinese-made AI chips and considering joint ventures for chip fabrication. Likewise, to substitute services like Google’s AI APIs, Russian equivalents (often by Yandex or Sber) have been promoted. The Deputy PM Dmitry Chernyshenko noted that the new AI Development Center will also focus on “overcoming risks of sanctions and independence from foreign platforms” . This is evident in areas like office AI software (e.g. speech-to-text for Russian, where homegrown APIs from Tinkoff or VK are now used instead of Google). Despite these efforts, integration with the global AI community persists where beneficial – for instance, Russian researchers still use open-source tools like PyTorch/TensorFlow (though there is interest in domestic frameworks), and some collaborations with foreign firms continue via neutral third countries. In essence, Russia’s digital infrastructure strategy for AI is about building a full-stack ecosystem internally: domestic clouds, open libraries, national data platforms, and custom software – all to ensure that AI progress can continue unhindered by external factors and aligned with Russian regulatory standards.

Connectivity and National Digital Infrastructure

Underpinning the AI revolution is the broader digital infrastructure – nationwide connectivity, internet bandwidth, and telecom networks – which Russia has been actively developing. The Digital Economy national program (2017–2024) invested heavily in expanding broadband internet access across Russia’s vast territory, rolling out fiber-optic lines and enhancing mobile network coverage . As a result, internet penetration and 4G coverage have improved markedly, providing the basic substrate for data generation and AI services (e.g., IoT sensors in agriculture or telemedicine in remote regions). The next leap is 5G networks: Russia identified 5G as critical for AI (enabling real-time data flow for autonomous vehicles, smart cities, etc.), and aimed to deploy 5G in all major cities by 2024. Progress has been slower than planned due to equipment access issues, but pilot 5G zones exist in Moscow, St. Petersburg, Kazan and others, often using domestically developed components or Chinese Huawei gear. The government has set localization goals for telecom equipment to reduce import dependence here as well. Additionally, Russia has built out GLONASS (its GPS-equivalent) and a network of satellites which, combined with AI, are used for tasks like precision agriculture and disaster monitoring .

The “Smart City” initiatives in Moscow and other large cities are a showcase of how digital infrastructure and AI intersect: Moscow’s CCTV camera network (one of the world’s largest) is AI-enabled for facial recognition and traffic analysis, requiring substantial cloud and connectivity support. By 2016–2017, Moscow and St. Petersburg had launched pilot projects for AI-driven urban services – from intelligent traffic light control to predictive maintenance of utilities . These efforts are now being standardized so that smaller cities can adopt them via cloud platforms . Another element is ensuring cybersecurity and reliability of infrastructure in the AI era. The government has beefed up the National Computer Emergency Response system and mandates security audits for critical AI systems (like power grid AI controllers or banking AI). Furthermore, as part of sovereign internet efforts, Russia tested run-of-the-country internet routing (RuNet) to ensure the network can function even if cut off externally – a controversial move but one that authorities argue could keep AI systems running in a crisis.

The pay-off of these infrastructure investments is already visible in the economy: by 2022, Russia’s AI market size reached ~₽650 billion, growing 18% from the previous year , and much of this growth is attributed to better connectivity enabling AI uptake in finance, retail, and government services. Looking ahead, the strategy projects that AI’s contribution to GDP will be huge – an estimated ₽11.6 trillion by 2030 (roughly 8-10% of GDP) – which assumes continued expansion of digital infrastructure to all sectors and regions. In summary, Russia’s approach to data, compute, and digital infrastructure is comprehensive: secure the data, build the compute and cloud at home, and lay the network pipes everywhere. By doing so, the country aims to provide a stable foundation for AI innovation and deployment, even amid global technological uncertainties. The ultimate vision is to have a fully self-reliant AI tech stack – from silicon to software – running on a highly connected, country-wide digital fabric, thereby cementing Russia’s autonomy and prowess in the AI age .

Part 3: Sectoral Integration and Global Influence

3A. Industrial Deployment & Tech Diffusion

Healthcare & Social Services

Moscow has operated one of the world’s largest city‐level AI deployments in radiology since 2020, running a formal “experiment” that integrates computer vision into public hospitals and clinics. City data indicate millions of studies have been processed by AI and that the portfolio now spans ~100 municipal AI projects across domains. See the city’s official updates on the radiology experiment and adoption statistics (mos.ru – March 2023; mos.ru – Dec 2023; mos.ru AI portal), and recent notes on expansion in 2024–2025 (open datasets for head CT – Feb 2024; mayor’s office – Feb 2025; news – Jun 2025). oai_citation:0‡Moscow City Services oai_citation:1‡ai.mos.ru

At the federal level, the Ministry of Health launched the national AI in Healthcare Platform to connect clinicians and developers, publish tasks and datasets, and enable accredited firms to access training data through controlled channels (ai.minzdrav.gov.ru; Ministry of Health – Nov 2022). The Ministry reports scaling across regions (e.g., use of voice documentation tools and virtual assistants, and registered AI medical products used as decision support systems), with periodic updates highlighting national pilots and the codification of an ethical code for medical AI (Ministry of Health – Feb 2024, Mar 2024, Ministry – Mar 2024, Ministry – Dec 2024, regional adoption – Jan 2025). oai_citation:2‡ai.minzdrav.gov.ru oai_citation:3‡minzdrav.gov.ru oai_citation:4‡futuredoc.minzdrav.gov.ru

Mobility, Logistics & Urban Management

Russia runs nationwide legal experiments for highly automated road transport. A Government resolution established an experimental legal regime (ELR) for testing driverless vehicles on public roads through 2025, with subsequent documents expanding conditions (e.g., remote dispatch, operation without a safety driver in the cabin in defined zones) and enabling robotaxi pilots in Moscow (Government resolution – Dec 2020; ELR conditions – Mar 2022; Government news – Mar 2022). In freight, state announcements highlight driverless KAMAZ truck operations on the M‑11 corridor as part of corridor automation pilots (Government news – Jun 2023). oai_citation:5‡Government of Russia oai_citation:6‡Government Portal

These transport ELRs complement sectoral sandboxes in aviation and urban services. For example, Government acts authorize trials of unmanned aviation systems and other digital innovations under controlled regimes (Government act – May 2024). At city level, Moscow continues to position itself as an urban AI testbed, adding platforms for full‑cycle AI model development and community portals that expose source code, datasets, and challenge tasks to local developers (platform launch – May 2024; Mos.Hub updates – Feb/Apr 2025, https://www.mos.ru/mayor/themes/12587050/). oai_citation:7‡Government Portal oai_citation:8‡Moscow City Services

Energy, Extractives & Manufacturing

Large state‑aligned industrial groups act as deployment champions. In oil & gas, Gazprom Neft reports using AI for chemical formulation (“digital molecules”), drilling optimization, and infrastructure monitoring; the company and partners also announced a national standard for AI in construction supervision (Gazprom Neft – Oct 2024; oilfield services – 2022; infrastructure monitoring – 2021; AI standard – Apr 2025). oai_citation:9‡Gazprom Neft oai_citation:10‡Gazprom Neft oai_citation:11‡Gazprom Neft oai_citation:12‡Gazprom Neft Digital

In the nuclear and utilities complex, Rosatom emphasizes AI for predictive maintenance, digital twins, and smart city/heat networks, noting dozens to over a hundred active projects across the industry and municipal services (Rosatom interviews – Jan/Feb 2024, https://www.rosatom.ru/journalist/interview/evgeniy-garanin-rosatom-vladenie-ai-tekhnologiyami-nashe-konkurentnoe-preimushchestvo/?sphrase_id=5728214; smart cities & heat – 2023, https://rir.rosatom.ru/media-center/news/sistemu-teplosnabzheniya-voronezha-osnastyat-iskusstvennym-intellektom/). Universities and industrial partners are also scaling workforce pipelines (e.g., ITMO master’s programs with Gazprom Neft) to support industrial AI deployments (Gazprom Neft–ITMO – Jun/Jul 2025, https://career.gazprom-neft.ru/about/events/gazprom-neft-i-itmo-zaymutsya-razvitiem-ii-obrazovaniya-v-ramkakh-natsproekta/). oai_citation:13‡rosatom.ru oai_citation:14‡rir.rosatom.ru oai_citation:15‡Gazprom Neft oai_citation:16‡career.gazprom-neft.ru

3B. Regulatory, Ethical & Safety Frameworks

Core Legal Instruments & Sandboxes

Russia’s foundational legal act is Presidential Decree No. 490 (2019), which approved the National AI Development Strategy to 2030 and set tasks for federal bodies to enable AI research, data access, and deployment. The decree has been updated repeatedly, including in February 2024, to reflect evolving priorities and implementation mechanisms (Decree No. 490 – Oct 2019; official PDF of the Strategy; Feb 2024 amendments). oai_citation:17‡Kremlin oai_citation:18‡Kremlin oai_citation:19‡publication.pravo.gov.ru

To accelerate trials and lower regulatory barriers, Russia legislated experimental regimes. Federal Law No. 123‑FZ (Apr 24, 2020) established a special AI regulation experiment in the city of Moscow, while Federal Law No. 258‑FZ (Jul 31, 2020) created a general framework for experimental legal regimes in digital innovation, amended further in 2024–2025 to broaden scope and tools (123‑FZ; 258‑FZ; 2024 amendment example; 2025 updates). oai_citation:20‡publication.pravo.gov.ru

Data Governance, Standards & Certification

Russia actively localizes international AI standards as national GOSTs and develops native frameworks. Examples include GOST R 59277‑2020 “AI systems. Classification”, and Russian adoptions of ISO/IEC AI standards such as GOST R ISO/IEC 22989‑2021 (Concepts and terminology) and GOST R ISO/IEC 23053‑2022 (Framework for ML‑based AI systems), which together underpin terminology alignment, quality benchmarks, and lifecycle management for AI systems (GOST R 59277‑2020; GOST R ISO/IEC 22989‑2021; GOST R ISO/IEC 23053‑2022). oai_citation:21‡geneva.mid.ru oai_citation:22‡Mid.ru

Ministries also publish implementation guidance and maintain registries supporting sovereign data access for AI training and deployment. Policy materials and project pages—e.g., the federal project “Artificial Intelligence” under the state digital programs—outline data availability mechanisms, sectoral datasets and model testing routes, and procurement levers (Ministry of Digital Development – federal AI project page; project “C3. AI” under Data Economy/Digital Transformation). oai_citation:23‡digital.gov.ru

Ethics, Transparency & Risk Management

Ethical and safety principles are articulated through policy instruments associated with Decree 490 (which references the Code of Ethics in AI) and sector‑specific codes (e.g., medical AI). Public documents stress transparency and explainability, human oversight, and responsible data use across the AI lifecycle (Decree 490 – ethics provisions). In healthcare, the Ministry has separately publicized an ethics code for AI in medicine as part of scaling certified tools across regions (Ministry of Health – Dec 2024). oai_citation:24‡Kremlin oai_citation:25‡futuredoc.minzdrav.gov.ru

3C. International Engagement & Standards

Multilateral Forums & Policy Positions

Russia’s stated approach in arms‑control and security discussions is to treat AI‑enabled systems within existing multilateral frameworks. At the UN CCW (Convention on Certain Conventional Weapons), Russia’s mission in Geneva has described the CCW as the appropriate venue for issues related to lethal autonomous weapons systems (LAWS), supporting continued expert work within that framework (MFA Mission Geneva statement – Nov 2020). oai_citation:26‡geneva.mid.ru

In parallel, AI features in broader foreign‑policy/economic fora such as BRICS. Government communiqués and ministerial briefings underscore AI’s contribution to growth and digital sovereignty across BRICS members, with recent estimates of macroeconomic impact and coordination across working groups (Government of Russia – Jun 2025; MFA overview – BRICS working group on security in ICT/AI – Nov 2024). oai_citation:27‡Government of Russia oai_citation:28‡Mid.ru

Standards Leadership & Technical Committees

Domestically, standards are coordinated through Rosstandart and relevant technical committees; Russia adopts ISO/IEC JTC 1/SC 42 AI norms as national GOSTs and complements them with native guidance (see GOST R 59277‑2020, GOST R ISO/IEC 22989‑2021, GOST R ISO/IEC 23053‑2022) to ensure common terminology, model lifecycle controls, and certification pathways applicable inside Russia while interoperable with international practice (docs.cntd.ru – GOST R 59277‑2020; GOST R ISO/IEC 22989‑2021; GOST R ISO/IEC 23053‑2022). oai_citation:29‡geneva.mid.ru oai_citation:30‡Mid.ru

Analytical bodies under the Government publicize and interpret these standards for implementers, providing summaries and sectoral guidance to accelerate adoption in industry and public services (Analytical Center under the Government – standards explainer). oai_citation:31‡ac.gov.ru

Regional & Bilateral Cooperation

AI cooperation threads through Russia’s broader regional diplomacy. BRICS and BRICS+ formats feature digital economy and AI as recurring items, with Government communiqués highlighting initiatives to scale digital public goods and foster cross‑border data‑driven services and innovation ecosystems (Government – Dec 2024 AI Alliance Network event; Government – program selections incl. BRICS+). oai_citation:32‡Government of Russia

Statements from the Ministry of Foreign Affairs emphasize secure and sovereign digital environments in multilateral workstreams and note the integration of AI into economic and security dialogues across BRICS members (MFA – BRICS digital agenda). oai_citation:33‡Mid.ru

3D. Defence, Security & Dual‑Use Considerations

Strategic Doctrines & National Security Planning

National‑level doctrine situates AI within broader technological sovereignty and security goals. The National Security Strategy (Presidential Decree No. 400, July 2, 2021) identifies the development and secure use of advanced digital technologies—including AI—as instrumental to defense capability and national resilience (Kremlin decree & PDF; official PDF). Periodic presidential and governmental acts subsequently reference AI in the context of national projects and sectoral security policies. oai_citation:34‡Kremlin oai_citation:35‡Kremlin

These strategic documents are linked to civilian AI policy via Decree 490 and its amendments, ensuring coherence between economic modernization and defense‑relevant technology development (e.g., secure supply chains for data/compute, trust in algorithms, and domestic standards) (Decree 490; Feb 2024 amendments). oai_citation:36‡Kremlin oai_citation:37‡publication.pravo.gov.ru

Defence R&D and Civil–Military Synergies

State corporations and industrial conglomerates report extensive use of AI for predictive maintenance, anomaly detection, and process optimization in dual‑use infrastructures (nuclear fuel cycle, power grids, petrochemicals), creating capability spillovers relevant to defense industrial readiness (Rosatom – interviews and program notes, https://rir.rosatom.ru/media-center/news/puteshestvie-umnykh-gorodov-rosatoma-v-mir-iskusstvennogo-intellekta/; Gazprom Neft – AI programs). oai_citation:38‡rosatom.ru oai_citation:39‡rir.rosatom.ru oai_citation:40‡ds.gazprom-neft.ru

Separate ELRs for autonomous systems (road vehicles, aviation, and maritime trials) sharpen regulatory clarity for safety assurance, C2 (command & control), and liability allocation—factors that are also important for defense use cases where human‑on‑the‑loop oversight and certification are required (Government – road AV ELR; ELR conditions – Mar 2022; Government – unmanned aviation ELR – May 2024). oai_citation:41‡Government of Russia oai_citation:42‡Government Portal

Export Controls, Critical Infrastructure & Resilience

Russia’s official stance in multilateral arms‑control debates favors rules‑of‑the‑road approaches within CCW rather than blanket bans on autonomous functions, while domestically prioritizing resilience of critical infrastructure and the trusted development and operation of AI in sensitive sectors (MFA Mission Geneva – CCW/LAWS; National Security Strategy 2021). oai_citation:43‡geneva.mid.ru oai_citation:44‡Kremlin

Sectoral authorities and analytical bodies under the Government continue to foreground security, safety and trust as prerequisites for mass AI deployment—linking safety standards, auditability, and certification to public procurement and rollout under national projects (Ministry of Economic Development – safety & trust brief, May 2025). oai_citation:45‡Economic Development Russia

Part 4: Performance, Resilience and Social Impact

Russia’s leadership frequently underscores AI’s importance at public forums, reflecting a national drive to measure progress, ensure resilience, build core capabilities, and secure public trust in AI. The National Strategy for AI Development in Russia places strong emphasis on tracking tangible outcomes and adapting to challenges. It is a sovereignty-driven plan focused on reducing dependence on foreign technology by investing heavily in domestic R&D and critical infrastructure . Part 4 examines how Russia monitors AI performance, prepares for future risks, develops foundational capabilities, and addresses the societal impact of AI.

4A. Performance Metrics & Monitoring

Russia has established specific performance metrics and monitoring mechanisms to gauge the success of its AI strategy and hold stakeholders accountable. An updated presidential decree in late 2023 introduced a comprehensive set of key performance indicators (KPIs) for AI development . These metrics span computing capacity, economic contributions, innovation output, human capital, public trust, and technology adoption. By articulating such targets, the government seeks to institutionalize data-driven oversight of the strategy’s implementation. Federal agencies are instructed to align their sectoral plans with the national AI strategy and report on progress, while regional authorities and state-owned enterprises are recommended to do the same . This ensures a whole-of-nation approach where AI advancement is continuously measured across levels of government and industry.

Crucially, Russia’s AI roadmap set time-bound goals for 2024 and 2030 to benchmark progress. By 2024, the country aimed to “significantly improve” its standing in AI, and by 2030 to eliminate its lag behind leading countries and attain global leadership in select AI domains . These milestones frame the strategy’s timeline and provide reference points for evaluation. In practice, mid-course assessments have led to adjustments: the 2023 decree explicitly acknowledged gaps and emerging challenges – such as hardware shortages, talent deficits, and low adoption in government – and updated objectives and metrics accordingly . This adaptive monitoring underscores Russia’s commitment to not only set goals but also refine its strategy based on measured performance and changing conditions.

Economic & Industry Impact Metrics

A core focus is measuring AI’s contribution to economic growth. The updated strategy quantifies expected GDP gains from AI: Russia projects that AI-driven technologies across all sectors will add about ₽11.2 trillion (approximately $109 billion) to its gross domestic product by 2030, a dramatic rise from just ₽0.2 trillion in 2023 . This implies AI could account for roughly 6% of GDP by the end of the decade, underscoring its envisioned role in the economy. To achieve this, authorities track metrics like the size of the AI market and investments. Annual investment in AI is targeted to reach ₽850 billion by 2030 (a seven-fold increase over current levels) . These indicators help policymakers adjust incentives and funding to meet the growth trajectory. They also monitor the formation of an AI industry: the strategy calls for creating a high-performance, export-oriented tech sector in key industries (e.g. manufacturing and agriculture) as a sign of success . In concrete terms, Russia aims to increase the number of organizations innovating with AI by 50%, expanding the ecosystem of companies contributing to AI development . Progress toward this goal is measured by tracking new startups, corporate AI initiatives, and partnerships formed under national programs.

Performance monitoring extends to sub-national and corporate levels. Regional governments have been encouraged to set AI adoption targets as part of their digital economy programs, aligning local economic growth with national AI indicators . Some large municipalities and republics have begun reporting the contribution of AI solutions to regional output and productivity. Likewise, the government keeps an eye on industry-specific metrics, such as AI use in priority sectors. For example, in healthcare (a critical social sector), a national program expects each region to deploy at least 12 certified AI solutions by 2030 to improve medical services . By embedding such targets, Russia’s strategy uses metrics not only to quantify AI’s macroeconomic impact but also to ensure broad-based industrial and regional growth through AI.

Research & Innovation Outputs

To evaluate its innovation ecosystem, Russia tracks research output and technological innovation metrics. The strategy emphasizes boosting the number and impact of scientific publications, patents, and technical solutions in AI. In fact, it calls for significant increases in citations of Russian AI research and in patents and applications developed by Russian scientists by 2024 . Academic output is an important barometer: the government monitors how often Russian AI papers are cited internationally and how many patents are filed domestically each year. These indicators gauge the quality and relevance of Russia’s R&D on the global stage. The creation of open-source AI libraries and tools is similarly tracked as a measure of innovation (and as a means to support wider adoption) . To encourage improvements, authorities have directed more funding to AI research centers and instituted awards for high-impact publications and breakthrough patents. The updated strategy also explicitly mentions using publication metrics to monitor research quality , signaling that bibliometrics and innovation indices feed into policy adjustments (for instance, reallocating R&D funds to underperforming areas or emerging fields in AI).

Russia’s current standing in AI research provides context for these efforts. While the country has a strong legacy in mathematics and engineering, it trails leading nations in AI innovation by many measures. As of 2019, Russia had only three supercomputers ranked in the world’s Top 500, compared to 228 in China and 117 in the United States – a proxy indicator of high-end research capability. Similarly, Russia has far fewer AI startups than the U.S. or China (around 168 identified startups versus thousands in those countries as of 2020) . Many international AI rankings until recently omitted Russia entirely, reflecting its late start in articulating a national AI strategy . These baselines drive the government’s resolve to catch up. Progress is noted: by one global index in 2024, Russia ranked 31st of 83 countries in AI innovation and investment environment – still behind not only the US and China but also fellow BRICS peers like India and Brazil. Aiming higher, Russian officials use such benchmarks to justify new initiatives. The state channels generous funding into flagship research projects (by Russian standards) and measures success partly by external recognition, such as Russian teams winning international AI competitions or collaborating on global R&D efforts. By continuously monitoring innovation outputs and comparing against global leaders, Russia’s strategy can identify gaps (e.g. fewer breakthrough publications or prototypes) and institute corrective programs (such as targeted grants or international partnerships).

Talent Development Indicators

Human capital development is a central pillar of Russia’s AI metrics. The strategy sets ambitious targets for expanding the AI-skilled workforce and educational outputs. Notably, it envisions that by 2030, 80% of Russian workers will have basic AI skills, a massive jump from only about 5% in 2023 . This sweeping goal reflects an intent to mainstream AI literacy across virtually the entire labor force, ensuring that employees in all sectors can utilize AI tools. Progress toward this is gauged by metrics like the number of people trained through AI courses, certifications earned, and participation in government-sponsored retraining programs. The Ministry of Education tracks enrollment in AI-related degree programs, with hundreds of universities now offering AI curricula or specialized programs . The strategy calls for introducing AI-focused modules at all levels of education and creating professional development and re-skilling programs in AI for existing workers . Success is measured in part by the count of graduates in AI and data science fields each year, as well as the establishment of “world-class” AI programs at top universities by the mid-2020s . By 2024, the number of AI specialists was expected to “grow significantly,” helping to alleviate talent shortages . These qualitative targets are now being supplemented with quantitative indicators, such as the proportion of STEM graduates specializing in AI and the retention rate of AI PhDs in the domestic industry or research sector.

Russia’s strong foundation in STEM education is an asset and a metric tracked by policymakers. In computer science and related disciplines (including machine learning), sixteen Russian universities made it into the global top-700 rankings in 2019 . However, only two – Lomonosov Moscow State and ITMO University – ranked in the top 100 worldwide, indicating a gap in elite educational performance . The government monitors such rankings and the international mobility of talent as indicators of success in its talent strategy. One explicit concern is “brain drain” – the loss of top AI researchers to tech hubs abroad. Thus, alongside counting how many specialists are trained, there is attention to retaining talent. Efforts like new AI research centers at institutes (e.g. the national AI center at MIPT) and competitive salaries in strategic labs are aimed at improving talent retention, which is qualitatively assessed through metrics like the number of expatriate Russian AI experts returning or the decline in outbound migration of graduates . Overall, by quantifying education and workforce development—from school programs to PhD output and worker retraining—Russia’s AI strategy treats talent as a measurable resource to be grown and tracked over time.

Adoption & Public Sentiment Metrics

Beyond economic and research metrics, Russia is also measuring the uptake of AI and public trust in the technology. The 2023 strategy update explicitly added “public trust” in AI as a performance metric, a notable inclusion that few countries quantify . This suggests the government may use surveys or public opinion indices to monitor how comfortable citizens are with AI systems (for instance, trust in AI-driven decisions in healthcare or security). Improving this metric would involve public outreach and transparency, and it is now on the radar of policymakers as an outcome to report. Similarly, enterprise AI adoption is tracked as an indicator of success . This could include the percentage of companies (especially large firms and critical infrastructure operators) implementing AI solutions in their operations, or the number of AI projects deployed in sectors like manufacturing, agriculture, finance, and government services. As a baseline, officials acknowledged that adoption in the public sector has been lagging – “low government adoption” of AI was listed among the challenges to overcome . In response, the strategy urges ministries and state agencies to pilot AI projects and integrate AI in service delivery, with progress measured by counts of AI use-cases launched and their impact (e.g. efficiency gains in public administration).

Concrete programs underpin these adoption metrics. For example, Russia’s Ministry of Digital Development has begun a national monitoring program for AI deployment in public services, requiring agencies to document and report AI implementations (“Incident No. 11” monitoring) . There are also guidelines for regional governments, which are expected to each implement a set number of certified AI solutions in domains like healthcare and urban management by 2030 . These acts both as mandates and metrics – the fulfillment rate of regions meeting the AI deployment quota is an indicator of nationwide diffusion. On the enterprise side, major state-run companies (from banking to oil & gas) have been directed to include AI development in their corporate strategies . The breadth of adoption is tracked through these firms’ reports, such as the share of business processes automated by AI or the investments each is making in AI. By monitoring such data, the government can quantify how deeply AI has penetrated the economy. The inclusion of public trust as a metric also reflects an understanding that widespread adoption hinges on societal acceptance. Issues like data privacy, algorithmic bias, or job displacement could erode trust; thus, any shifts in public sentiment (for instance, as measured by polling on attitudes toward AI in daily life) are taken as feedback on the strategy’s social viability. In summary, Russia’s performance monitoring framework extends beyond raw output to include qualitative success factors like trust and diffusion, ensuring that the AI revolution is tracked not only in labs and GDP figures, but in workplaces and communities as well.

4B. Strategic Foresight & Resilience

Russia’s AI strategy is designed with strategic foresight and resilience in mind, acknowledging that technological progress occurs in a dynamic, often adversarial global context. To secure its AI ambitions, Russia must anticipate geopolitical and economic disruptions – a reality brought into sharp focus by recent events such as international sanctions and the war in Ukraine. The national AI plan therefore includes measures to future-proof development against external shocks and internal weaknesses. This involves identifying critical bottlenecks and dependencies early, investing in domestic alternatives, and adjusting plans through scenario analysis. In practice, Russia emphasizes achieving technological sovereignty in AI – ensuring the supply of key inputs like hardware, data, and talent even if cut off from foreign sources . The government also seeks resilience through diversifying partnerships (e.g. aligning with friendly nations in AI efforts) and through centralized coordination that can marshal resources quickly in response to crises . Strategic foresight units within ministries and the Security Council regularly assess trends in AI (such as shifts in global regulatory norms or breakthroughs in areas like generative AI) to update national priorities. By embedding such forward-looking mechanisms, Russia aims to avoid strategic surprise and sustain momentum toward its 2030 AI goals even under difficult conditions.

At the same time, resilience in the Russian context also means balancing aggressive pursuit of AI with cautionary oversight. The updated strategy integrates new provisions on AI safety, ethics, and risk management frameworks , which can be seen as a hedge against potential social or security risks of AI deployment. There is recognition that unregulated AI could backfire (through accidents or public backlash), undermining progress. Thus, Russia is crafting standards and encouraging “trusted AI” in high-risk applications as part of resilience . Additionally, strategic foresight extends to military dimensions: the Russian defense establishment is closely involved in AI planning to ensure the country is prepared for the next generation of warfare. Lessons from current conflicts are informing the integration of AI in defense, and conversely, military needs are prompting more centralized national coordination in AI R&D . Overall, strategic foresight and resilience measures in Russia’s AI strategy reflect a realism about the hurdles ahead – from silicon shortages to ethical dilemmas – and a resolve to navigate them through sovereign capability building and adaptive governance.

Confronting Sanction Constraints

One of the most immediate tests of Russia’s AI resilience has been the wave of Western sanctions and export controls targeting high-tech imports. These restrictions, intensified since 2022, have cut off Russia from many advanced semiconductors and other critical components needed for AI development . Major chip producers in the US, EU, Taiwan, Japan, and South Korea have halted exports of cutting-edge GPUs (graphics processing units) and related equipment to Russia, creating a significant hardware bottleneck. President Putin and other officials openly acknowledge that sanctions “sorely limit” Russia’s AI ambitions in the near term . The CEO of Sberbank, German Gref – who spearheads much of Russia’s AI push – admitted in 2023 that acquiring high-performance GPUs was the “trickiest” hurdle for their AI projects . These GPUs are essential for training AI models, and their scarcity slows progress in both research and deployment of AI systems. In response, Russia has had to prioritize resilience tactics: stockpiling available hardware, repurposing less advanced chips, and optimizing software for efficiency. The government is closely monitoring domestic computing capacity as a metric of resilience; indeed, “supercomputer power” was added as a key performance indicator in the strategy , reflecting the need to track and boost computing resources under sanction constraints.

Facing these challenges, Russia’s foresight planning includes worst-case scenarios of prolonged tech isolation. Officials concede that the country is currently dependent on foreign (especially U.S. and Chinese) hardware for AI, and thus vulnerable . This has influenced strategic decisions – for example, accelerating efforts to develop indigenous chips and fostering relationships with any remaining suppliers not aligned with the sanctions regime. The impact of sanctions is also evident in international rankings: Russia’s relative position in the global AI race has been hampered by limited access to cutting-edge hardware and capital. As noted, Russia stands well behind the top AI powers by composite indices , and its gap in semiconductor capability is a key reason. However, resilience is gradually improving. By mid-2025, Russian entities have found creative means to obtain certain components via third countries and are focusing on slightly older but available node technologies for AI training. The strategic calculus assumes that sanctions will be a long-term reality, so the country is adapting by prioritizing stability over state-of-the-art – aiming to keep AI progress steady with what hardware can be obtained or produced. This adaptation is a direct outcome of strategic foresight: anticipating continued restrictions, Russia is reorienting its AI goals to be achievable with a constrained tech stack, thereby ensuring the program can endure external pressure.

Technological Sovereignty & Import Substitution

To reduce vulnerability, Russia has doubled down on technological sovereignty programs that seek to replace foreign technologies with domestic ones. Import substitution is a national priority not just in AI but across the tech sector, and it features prominently in AI strategy execution . In practical terms, this means large investments in indigenous semiconductor research, supporting local AI software ecosystems, and nurturing homegrown talent – all to decrease reliance on Western or other external inputs. The government has funneled billions of rubles into projects for developing Russian-made AI chips and high-performance computing systems. State-backed entities like Rostec and the Skolkovo Foundation are funding new ventures in chip design, while existing Russian chip designers (MCST, Baikal Electronics, and others) are being leveraged to create AI-capable processors. Although these efforts have yet to yield components on par with NVIDIA or AMD, some progress is noted: for example, prototype neural network accelerators and improved microelectronics fabrication capabilities at facilities like Mikron. The strategy’s resilience metrics include tracking the share of domestic components in AI systems – a percentage that the government is committed to raising year by year.

Concurrently, Russia is enhancing its domestic AI software and platform stack to mitigate dependence on foreign software frameworks or cloud services. Initiatives encourage Russian developers to build and maintain open-source machine learning libraries and tools, often as alternatives to popular Western ones like PyTorch or TensorFlow . This is both a foundational capability effort and a resilience measure: if geopolitical rifts widen, Russia wants its AI ecosystem to function autonomously. The updated strategy explicitly cites “technological sovereignty” as a guiding principle, putting it on par with security and economic growth . Achieving this involves not only creating local technologies but also legal and financial steps: for example, preferential procurement policies that require government agencies and state firms to use domestically developed AI solutions where possible. There are also import substitution quotas mandating that a certain percentage of software used in critical infrastructure be Russian-made. Progress is measured by these quota fulfillments and by a decline in the use of foreign AI APIs and cloud platforms in Russian projects.

In the interim, Russia is not going it entirely alone – it is leaning on less hostile partners to fill some gaps. Partnerships with China are especially pivotal. China remains willing to supply certain high-end components (within the limits of its own trade restrictions) and to collaborate on technology. Russian officials openly state that they will rely more on Chinese advancements in AI hardware and even policy coordination . This has been seen in joint projects (e.g. Huawei partnering with Russian institutions on AI R&D ) and Russia adopting Chinese vendors for critical needs (such as cloud infrastructure equipment). While this reliance is a stopgap rather than true self-sufficiency, it bolsters resilience by opening an alternative pipeline of technology. The strategic calculus is that, by the time Chinese support might wane or Chinese tech faces its own bans, Russia’s import substitution efforts will have matured enough to stand on their own. In summary, Russia’s quest for technological sovereignty is a multi-faceted resilience strategy: invest domestically, substitute imports, and bridge the gap through friendly partnerships – all closely overseen with milestones and state support to ensure the AI agenda can survive external cutoffs.

Centralized Coordination & Integration

Another resilience strategy has been to centralize AI policy coordination and improve integration across the civilian and defense sectors. Learning from the lag in early AI efforts, Moscow has moved to strengthen top-down oversight so that resources are efficiently allocated and duplicated work is minimized. In late 2023, the government established a new centralized AI authority – effectively an AI policy agency at the federal level – to coordinate implementation of the national strategy . This body harmonizes efforts across ministries, monitors progress on the strategy’s KPIs, and can propose regulatory or budgetary adjustments in response to implementation issues. By having a dedicated central hub, Russia aims to increase agility and strategic coherence, which is vital in a fast-moving field like AI. All federal ministries have been directed to follow the national AI strategy guidelines when drafting their own digital transformation plans , ensuring unity of purpose. State-owned enterprises (which in Russia control large swaths of the economy) are likewise mandated to incorporate AI development into their corporate strategies . This effectively extends centralized coordination into major industries via corporate governance – those companies must align with national objectives, and many have internal AI units that coordinate with the central AI agency. The result is a more “whole-of-government” approach to AI, which is resilient in that it concentrates efforts and can rapidly redirect focus if a particular approach fails or a new priority emerges.

The war in Ukraine and the accompanying pressures have also spurred greater civil-military integration in AI, as a resilience measure. Russia’s military leadership, confronted with modern warfare challenges, recognized the need to leverage the country’s civilian tech talent and companies to stay competitive. Consequently, the government is “forcing greater cooperation between the country’s military and civilian sectors” in AI development . This is evident in joint R&D initiatives where defense research institutes partner with private AI firms or academia on projects like autonomous drones and AI-driven surveillance systems. The Ministry of Defense now participates in national AI forums and planning sessions, ensuring military requirements inform the broader strategy. From a resilience perspective, such integration means Russia can pool resources and expertise across domains, and that innovations flow both ways (military algorithms finding civilian use and vice versa). It also builds redundancy: if private sector innovation slows due to market issues, defense labs might pick up slack, and if sanctions hit military programs, the civilian sector’s work can provide substitutes. The centralized AI council or agency often mediates this integration, identifying overlaps and opportunities between, say, a civilian facial recognition project and a security application. The ultimate goal is a unified national AI effort that can weather budget reallocations or crises because it’s not siloed. As Samuel Bendett noted, the government’s recent steps accelerate a centralized approach and explicitly knit together defense and civilian AI efforts to ensure national objectives are met despite wartime disruptions .

Alliances and International Collaboration

Resilience is also pursued through international collaboration and alliance-building in AI, which serve to mitigate isolation. Facing a largely closed door to Western AI ecosystems, Russia has looked to the BRICS and other emerging economies to create a cooperative network. In December 2024, President Putin announced the formation of an “AI Alliance Network” among BRICS members (Brazil, Russia, India, China, South Africa) and additional interested countries . This alliance is envisioned as a platform for joint research, talent exchange, and shared development of AI technologies and standards outside Western dominance. By teaming up with partners that are either neutral or friendly, Russia can access a broader pool of expertise and even markets for AI products. For instance, the alliance would allow Russian AI firms to pilot or sell their solutions in member countries, offsetting the loss of Western markets . It could also facilitate sharing of data and resources – a critical advantage if, say, Russia needs diverse datasets or additional cloud computing power that partners can provide. Strategically, this move is about strength in numbers: aligning with China and other players could improve Russia’s standing and bargaining power in the global AI race . Indeed, analysts have suggested that a tighter AI partnership with China might alter the balance of the AI competition, giving Russia access to some of China’s advancements and vice versa .

Furthermore, Russia participates in international fora to shape norms in a way that supports its resilience and strategic interests. It has joined discussions at the United Nations and other bodies on AI ethics and governance. Notably, Russia has opposed blanket bans on certain military AI applications (like lethal autonomous weapons), arguing instead for careful regulation . At the same time, it voices support for developing “clear universal rules and ethical norms” for AI use globally , which signals an interest in being part of rule-making so that those rules don’t undercut its capabilities. This diplomatic engagement is a form of strategic foresight: Russia wants to avoid international agreements that could constrain its AI development (especially military-related), and to encourage norms that legitimize its approach (such as sovereignty over data, or the principle of technological non-discrimination in trade). In groups like UNESCO and the Global Partnership on AI (where Russia might have observer roles or indirect involvement), it pushes narratives of inclusive development and “digital sovereignty”. For example, joint statements in BRICS context emphasize inclusive participation and equitable access to AI for all nations – language that counters the idea of isolating any country technologically.

One tangible outcome of these collaborations is knowledge exchange: Russian researchers now work with Indian and Chinese counterparts on AI projects, share best practices on AI in governance with South Africa and Brazil, and collectively invest in flagship initiatives (such as cross-border innovation hubs). This multilateral approach adds resilience by giving Russia alternative channels for innovation when bilateral ties with the West are severed. If one country develops a breakthrough (say, India in a certain AI application), Russia could benefit through the alliance. It’s also a hedge against unilateral pressure – forming a bloc makes it harder for any one outside power to dictate AI norms or exclude Russia entirely. In summary, through alliances like the BRICS AI Alliance and active participation in shaping global tech governance, Russia is crafting an external environment that can support its AI ambitions and buffer against attempts to thwart them . The success of this strategy will depend on how robust these partnerships become, but as a foresight measure, Russia has clearly signaled that it sees its AI future as part of a non-Western coalition forging its own path.

4C. Foundational AI Capabilities

Building robust foundational AI capabilities is a cornerstone of Russia’s strategy, aimed at establishing the essential building blocks for AI advancement under national control. This entails developing competencies and infrastructure across hardware, data, software, and core algorithms. The overarching goal is to cultivate an independent AI “tech stack” – from semiconductor chips and supercomputers to frameworks and large-scale AI models – so that Russia can innovate in AI without critical dependencies on foreign technology . By investing heavily in domestic R&D and capacity, Russia seeks to both catch up with global leaders and insulate its AI ecosystem from external shocks. President Putin’s 2019 decree (No. 490) explicitly framed AI development as a matter of technological sovereignty and national security, setting the stage for sustained funding into these foundational areas . The strategy identifies improving hardware for AI, expanding high-quality data access, and creating open-source libraries as priority tasks alongside training talent . Significant resources have since been allocated to supercomputing centers, national data platforms, and flagship research projects to develop Russia’s own AI algorithms. The idea is that by 2030, Russia should possess a self-reliant AI ecosystem: powerful domestic compute infrastructure, ample and well-organized data, locally-developed AI software tools, and a suite of Russian-made AI models addressing national needs. Below we examine each of these foundational pillars and Russia’s progress toward strengthening them.

Compute Infrastructure & Supercomputers

At the core of AI capability is compute infrastructure – the servers, supercomputers, and cloud platforms that enable model training and deployment. Russia has recognized that its AI aspirations depend on dramatically scaling up compute power. The national strategy thus includes specific initiatives to expand high-performance computing (HPC) capacity dedicated to AI. One metric introduced in 2023 is the total supercomputing performance available domestically for AI R&D . To boost this, major investments have gone into building new supercomputer clusters. For example, Sberbank – one of the key drivers of AI in Russia – launched the “Christofari” AI supercomputer in late 2019, which at the time was among the most powerful in Europe with ~6.7 petaflops of performance . This system was specifically designed to train neural networks and signaled Russia’s intent to provide world-class infrastructure for its researchers. Since then, Sberbank has upgraded its HPC (adding the “Christofari Neo” with NVIDIA A100 GPUs in 2021), and other entities like the Moscow State University and the Russian Academy of Sciences have also deployed large computing systems for AI. The cumulative effect is gradually improving Russia’s rank in global HPC – though as noted earlier, only a few Russian machines have made the Top500 list so far . The government monitors the total petaflops of AI-dedicated compute and uses it to set targets (e.g. aiming to double or triple capacity within a certain number of years).

Because of sanctions blocking cutting-edge chips, Russia’s strategy for compute infrastructure also involves creative solutions to ensure supply. One aspect is developing indigenous AI accelerators and leveraging older-generation hardware that can still perform AI tasks at scale. There have been research programs into Russian-made GPUs and FPGAs that could handle machine learning workloads. Additionally, efforts are being made to cluster and network existing servers more efficiently. The strategy calls for “creating demand for cloud computing” services domestically and providing discounted access to computing resources for researchers, students, and startups . This is essentially an attempt to maximize utilization of what hardware is available. By subsidizing cloud compute time, the government lowers the barrier for AI developers who need significant compute but might not afford it. It also encourages the growth of Russian cloud providers. Indeed, companies like Yandex and SberCloud have scaled up their cloud platforms to offer AI-as-a-service within Russia. The state often partners with these providers to handle workloads from academia and smaller firms. Monitoring of supply and demand dynamics for compute is built into the strategy – a nod to ensuring that as more organizations start AI projects, the infrastructure can meet their needs. Finally, realizing that hardware self-sufficiency is a long game, Russia is bolstering resilience of its compute infrastructure: building data centers with domestically produced components where possible and working on replacing critical imports (like high-end networking gear or cooling systems) with local alternatives. In summary, expanding compute power for AI is both a quantitative race and a strategic imperative for Russia, and the country has instituted programs to track progress (in flops and machines installed) and to steadily advance this foundational pillar despite external constraints.

Data Ecosystems & Access

Another foundational element is the availability of large, high-quality datasets and data infrastructure for AI. Recognizing that data is the fuel for machine learning, Russia’s strategy places importance on improving data access, sharing, and governance. One of the updated objectives is “improving data quality and availability” across the economy . This involves several parallel efforts. First, the government is working on aggregating public data into centralized platforms. Russia has vast stores of data from its governance systems (e.g. medical records, education, social services, transportation) and the goal is to make these accessible for AI applications while respecting privacy and security. The concept of a national data lake or integrated datasets for AI training has been floated. For instance, the healthcare ministry integrated over 1.4 billion electronic medical records by 2024 into a unified system , providing a rich resource for developing AI diagnostics. Similar integration is ongoing in areas like tax/customs data for economic AI models or satellite imagery for agricultural AI. The strategy monitors how many datasets are opened up or created for AI purposes each year, as well as their usage by developers.

Second, to encourage data sharing, Russia is developing data marketplaces and repositories. The decree update mentions enabling repositories of data and AI solutions domestically . An example is the “Gosdata Hub,” a portal for public datasets, and sector-specific repositories (such as for autonomous vehicle training footage or bilingual text corpora for language models). By curating and distributing key datasets, the government helps smaller players who might not have data collection capabilities. Russia is also setting data standards and interoperability frameworks as foundational work – particularly in fields like healthcare, where over 30 AI-specific standards (including for data formats) have been introduced to ensure data from different hospitals or regions can be combined for AI analysis . The emphasis on interoperability and standardization is to avoid data silos that would fragment the national AI effort.

However, it’s acknowledged that data quality is a challenge; many Russian datasets historically suffer from inconsistencies or gaps. Thus, part of the foundational agenda is data cleaning and annotation initiatives. Programs have been funded to label large datasets (for example, annotating medical images with diagnoses to build training sets for AI). The strategy’s success in data readiness might be measured by improvements in AI model performance that can be attributed to better data, or by external benchmarking (such as Russia’s standing in global open data indices). Notably, the state asserts a strong role in data governance: ensuring the government “has priority access to public data” for AI needs, even if that data is broadly defined . This sometimes raises privacy concerns, but from the strategy perspective, it guarantees that valuable data (like telecom records or internet user data) can be tapped for national AI projects. In short, Russia is building the data foundations through centralization, standardization, and state-facilitated sharing, aiming to turn its large population and legacy of record-keeping into an asset for AI development. Progress will be reflected in the increasing volume and diversity of data available to domestic AI researchers – a metric quietly tracked via the number of data portals launched and datasets published under national programs.

Software Ecosystem & Open-Source Tools

Developing a sovereign software ecosystem for AI is equally crucial. Russia’s strategy encourages the creation and adoption of domestic AI platforms, frameworks, and libraries. This is partly driven by necessity (concerns over relying on foreign software that might become unavailable) and partly by opportunity (building competitive products in the global AI software market). A key initiative was the drafting of an AI Code of Ethics and associated guidelines for software developers, which, while focusing on ethical conduct, also helped consolidate the AI developer community around shared goals . On the technical side, the government has supported projects like Sber’s open-source library for natural language processing and Moscow Institute’s DeepPavlov conversational AI library. The strategy explicitly highlights domestic AI software development and open-source libraries as priorities . By nurturing open-source contributions, Russia hopes to reduce dependency on Western repositories (like GitHub) and foster homegrown innovation that anyone in the country can use and build upon. The 2023 decree update reinforces this by calling for developing open-source libraries and repositories of AI solutions in Russia . Metrics here include the number of open-source AI tools published by Russian teams and their uptake (downloads or usage) by industry and academia.

There is also an effort to build Russian alternatives to popular AI development frameworks. While TensorFlow and PyTorch remain widely used (and are open-source themselves), Russian tech giants are creating their own. For instance, Yandex has worked on CatBoost (an open-source algorithm library for machine learning), and more ambitiously, there are attempts to create a Russia-centric deep learning framework optimized for Russian hardware and languages. These attempts are tracked in terms of performance benchmarks and adoption. The government often provides grants or challenges to accelerate such software. Moreover, preference in public procurements is given to AI software developed in Russia, effectively boosting demand for local products.

The ecosystem extends to AI cloud services and APIs. SberCloud’s AI platform and Yandex’s AI Cloud are promoted as national champions to serve domestic needs in computer vision, speech tech, etc., via APIs – reducing the reliance on Google’s or Microsoft’s AI services. The state has even at times restricted certain foreign software in sensitive sectors, indirectly pushing users toward local solutions. The strategy measures success in this domain by increased self-reliance: for example, a higher percentage of AI projects in Russia using Russian-made software stacks year over year. It also celebrates milestones like when Russian frameworks reach global quality parity or when local startups become exporters of AI software. The creation of “solution repositories” in Russia is meant to catalog domestic AI algorithms and codebases, making it easier for companies to adopt them rather than seeking foreign solutions. All these steps contribute to a resilient and thriving domestic AI software environment – a foundational layer that Russia views as just as important as hardware and data in achieving AI leadership.

Emerging AI Models & Applications

As the culmination of efforts in hardware, data, and software, Russia is pushing the development of its own large-scale AI models and applications that can serve national needs and signal international competitiveness. The strategy has shifted in recent years from primarily importing or adapting others’ AI technologies to creating original AI systems – particularly in the realm of generative AI and foundation models. By 2023–24, Russia became one of about ten countries that have developed their own large generative AI models . Sberbank introduced GigaChat, a conversational AI model intended as a Russian counterpart to ChatGPT . Likewise, Yandex has unveiled YaLM (YandexGPT) models for text generation . These models are trained predominantly on Russian-language data and designed to understand Russian context and culture better than foreign models. Their emergence is a point of pride and a strategic asset; as Reuters reported, having indigenous generative AI gives Russia the potential to become a more significant AI player, not entirely dependent on imported tech . The government likely tracks the progress of these models through benchmarks (like their performance on language tasks) and adoption (such as integration of GigaChat into Sber’s services or deployment of YandexGPT across Russian apps). The number of users and organizations using domestic AI models is a new metric of success – aiming to see Russian models widely used inside the country and even offered abroad to friendly markets.

Russia is also channeling AI capabilities into priority applications that align with national interests. The 2019 strategy and subsequent documents stress focusing on areas like defense, security, and critical infrastructure . This means a lot of foundational AI work is geared towards, for example, computer vision for surveillance and border security, decision-support systems for the military, or predictive maintenance for oil & gas infrastructure. Achievements in these domains are often kept low-profile, but they are strategically important and funded accordingly. On the civilian side, there is strong impetus to apply AI in sectors that drive economic growth or public well-being: smart city initiatives, healthcare AI, agricultural tech, and transportation. Foundational efforts have yielded AI systems like computer vision algorithms for traffic monitoring in Moscow, diagnostic AI tools reading medical scans in regional hospitals, and pilot projects for autonomous farming equipment. The breadth of these applications is expanding as foundational capabilities improve. A notable example of bridging foundational work to application is the healthcare sector: Russia launched a national AI in medicine program that has already certified AI systems for clinical use and expects every region to implement a dozen of them by 2030 . This top-down approach ensures that as soon as a model is mature (foundation achieved), it is rolled out widely to deliver value.

The strategy also recognizes the significance of exportable AI products as a measure of capability. It envisions creating an export-oriented sector equipped with modern AI technologies . Already, Russian companies are marketing AI software in areas like facial recognition (e.g. NTechLab’s FindFace) and cybersecurity to foreign clients. The success of these on the international market is both an economic win and a validation of Russia’s foundational strength. Metrics here include the number of countries adopting Russian AI solutions or the revenue from AI product exports. Finally, the existence of a robust foundation is meant to allow Russia to respond quickly to new AI trends. When generative AI surged globally, Russia leveraged its research base to debut competitive models within a year. If tomorrow there is a breakthrough in, say, quantum AI or neuromorphic computing, the country wants to have the foundational pieces (skilled researchers, compute power, etc.) ready to pivot and contribute. This agility is a hallmark of a mature AI capability foundation and is something Russia is striving to attain by systematically fortifying each layer of its AI ecosystem.

4D. Public Trust, Inclusion & Social Equity

A successful national AI strategy must not only drive innovation but also ensure that AI is developed responsibly and inclusively, earning the trust of the public. Russia’s approach to public trust, inclusion, and social equity in AI reflects a combination of top-down guidance and multi-stakeholder engagement. The government has articulated ethical principles to guide AI development, signaling that human rights, safety, and fairness should be considered alongside technological and economic goals . At the same time, Russia’s strategy emphasizes making the benefits of AI accessible across society – geographically, demographically, and across sectors – so that AI does not remain an elite technology but contributes to broad social welfare. This includes initiatives to reduce the digital divide between urban centers and outlying regions, and to use AI in tackling social challenges like healthcare access and education quality.

Public trust in AI is seen as both an end and a means: a trusting society is more likely to embrace AI solutions (improving adoption metrics), and public buy-in is necessary for AI to achieve its transformative potential. To cultivate this trust, Russian authorities are actively involving key stakeholders – leading tech companies, academia, and civil society representatives – in formulating ethical guidelines and best practices. Notably, Russia opted for a voluntary code of AI ethics rather than heavy-handed legislation at the outset, aiming to foster a culture of responsibility within the AI community . This approach relies on transparency and soft governance to build trust gradually. Additionally, the state orchestrates public communication around AI, highlighting success stories and national progress in media to shape a positive narrative. While Russia’s political context is unique (with state-aligned messaging playing a strong role), the underlying principle is that people should see AI as aligned with national progress and their own interests, not as a mysterious or threatening force. Inclusion and equity are addressed through programs to spread AI resources – like training and tools – to underserved areas and groups. The following sub-sections discuss how Russia is operationalizing ethics, engaging the public, ensuring regional inclusion, and striving for equitable AI outcomes.

Ethical Principles & Governance

In October 2021, Russia took a significant step toward fostering AI ethics by introducing its first national Code of Ethics for Artificial Intelligence . This code, signed by the AI Alliance (a coalition of major tech companies like Sberbank, Yandex, Gazprom Neft, and others) and government bodies, lays out general principles and standards for the design, implementation, and use of AI. It covers issues such as respecting human rights, ensuring the transparency of AI interactions (e.g., identifying AI in communication with people), data privacy, and preventing misuse of AI technologies . Importantly, the code is voluntary – companies and organizations can choose to commit to it – reflecting a governance approach that seeks to encourage ethical behavior without stifling innovation through strict regulation . By 2021’s end, over 100 organizations had pledged to adhere to the AI Ethics Code . The code has since been integrated into the national “Artificial Intelligence” federal project and aligns with Russia’s broader Strategy for Developing the Information Society (2017–2030) , giving it an official imprimatur and making ethics part of the long-term digital development agenda.

The highest levels of government have endorsed ethical AI governance. President Putin himself called for the development of an “internal moral and ethical code” for AI, emphasizing the need for professional and business communities to partake in shaping these principles . This political backing ensures that ethical guidelines are taken seriously. In practice, a government Analytical Center collaborated with the AI Alliance and the Ministry of Economic Development to author the ethics code , symbolizing a public-private partnership in governance. Adherence being voluntary means that rather than immediate legal enforcement, the code’s effectiveness relies on industry buy-in and perhaps peer pressure among Russia’s tech firms to uphold a good reputation. It is likely that over time, elements of the code will inform regulations or standards. In fact, the national strategy’s 2023 update strengthens the governance framework: it calls for establishing a “comprehensive legal framework” for AI and mandates that trusted AI systems be used especially in high-risk scenarios . This suggests movement from purely voluntary ethics to more formal oversight. Already, updates to the strategy include provisions to balance public and private interests and to institute safety standards for AI . For example, Government Decree No. 140 in healthcare provides detailed rules for AI in medicine, ensuring patient safety and data protection .

Ethical governance in Russia also has an international dimension. Russia contributes to global discussions on AI ethics – for instance, it supported UNESCO’s Recommendation on the Ethics of AI in 2021 and engages in UN forums about autonomous weapons ethics . Domestically, one sees efforts to operationalize ethics: the introduction of AI ethics committees in large companies, ethical review requirements for certain AI research, and even a Code of Ethics in Medicine specifically for medical AI (noted as a world-first sector-specific AI ethics code) . These measures build a multi-layered governance ecosystem: high-level principles, sectoral standards, and organizational best practices. The ultimate aim is to create a “space of trust” around AI in Russia – a phrase used by some policymakers to describe an environment where developers, users, and regulators have confidence in AI. While challenges remain (ensuring compliance to a voluntary code, updating laws to address new AI risks), Russia’s early adoption of an AI ethics code and its inclusion of ethical norms in strategic documents demonstrate a clear recognition that public trust starts with a strong ethical compass in AI development.

Public Awareness & Engagement

Russia’s government has undertaken efforts to shape public perception of AI and engage society in the AI discourse, understanding that awareness is key to trust and adoption. One major avenue has been high-profile events and media campaigns that highlight the benefits of AI. The annual AI Journey conference, hosted by Sberbank in Moscow and often attended (virtually or in person) by President Putin, is a prime example. These events are widely covered on state media and presented as showcases of how AI is driving economic development, improving services, or boosting Russia’s global status . For instance, news broadcasts might feature AI triumphs like a Russian AI beating humans in a certain task, or how AI is used to optimize urban traffic in a city. Such stories are meant to familiarize the public with AI’s positive impacts. Additionally, the forum titled “Ethics of Artificial Intelligence: The Beginning of Trust” – where the ethics code was signed – was deliberately public-facing and had extensive press coverage . By organizing an international forum on AI ethics and broadcasting it, the authorities signaled to citizens that they are proactively addressing AI’s societal implications and not just hyping the technology. This kind of communication is intended to preempt fear or suspicion by showing that AI development in Russia is accompanied by ethical considerations and global dialogue.

Public engagement is also pursued through education and AI literacy initiatives. Recognizing that an informed public is more likely to trust AI, Russia has included AI topics in educational curricula and public lectures. From introducing basic AI concepts in school programs to encouraging universities to host open courses and competitions in AI, the strategy aims to demystify AI for the average person. In 2022, for example, the “AI Academy” project was launched to provide online AI courses for various age groups, and a series of hackathons and innovation challenges have been opened to the public to stimulate grassroots participation. The measure of success here is the growing number of people who have at least a basic understanding of AI. Government agencies also solicit input from industry and academia on pending AI regulations or standards (though broader public comment periods are not as institutionalized as in some countries).

When concerns about AI do arise, Russian authorities have shown responsiveness, if only to maintain confidence. One notable issue globally has been algorithmic bias and discrimination. In Russia, an example was public worry about the potential for AI systems to unfairly deny loans or screen job applicants. While not widely publicized, regulators quietly engaged banks and tech firms to ensure AI models undergo fairness testing, and they’ve signaled willingness to intervene if citizens face systematic bias from AI. Another example is privacy: with AI relying on big data, citizens have latent concerns about how their personal information is used. The Russian data protection law (and the ethics code) demand transparency in AI data usage, and some officials in communications watchdog Roskomnadzor emphasize this in public statements to reassure people.

Moreover, stakeholder engagement has been part of the strategy’s formulation. The process of developing the national AI strategy and subsequent updates involved consulting experts from academia and industry (though civil society input was limited). The existence of the AI Alliance – a consortium of major tech companies – means that these companies effectively represent user interests to some extent, since they gauge public sentiment for their products. Through the Alliance, feedback from the private sector about public expectations or concerns is channeled to policymakers. In summary, Russia’s approach to public awareness and engagement is somewhat top-heavy (state-driven narratives and company-led forums), but it aims to build familiarity and confidence. By highlighting AI’s role in national progress, responding to ethical issues, and involving key stakeholders in conversations, the government strives to cultivate an informed public that sees AI as an opportunity. The very framing of AI as crucial for national prosperity and security – a theme often repeated by Putin – is meant to translate into popular support, as patriotic pride is attached to succeeding in AI. Time will tell how deeply this messaging resonates, but for now, AI is being woven into the public consciousness in Russia through orchestrated engagement efforts.

Regional Inclusion & Digital Access

Given Russia’s vast geography and regional disparities, the national AI strategy explicitly seeks to ensure that AI development and its benefits are spread beyond the major tech hubs of Moscow and St. Petersburg. A key policy in the 2023 update was the recommendation that regional and local governments incorporate AI into their development plans . This means that each region (republic, krai, or oblast) is encouraged – and informally expected – to craft initiatives for AI in local industries and public services. The national government provides frameworks and sometimes funding for this. For example, several regions have launched their own AI roadmaps aligning with the national strategy, focusing on local strengths (one region might emphasize AI in agriculture, another in mining or logistics depending on their economic base). The performance of these regional efforts is monitored; the Ministry of Economic Development tracks which regions are most actively implementing AI projects and shares best practices among them. The goal is not to have AI only in wealthy urban centers, but also in, say, Tatarstan’s agriculture or Primorsky’s ports.

Infrastructure programs are also oriented toward inclusivity. Under the “Digital Economy” national program, high-speed internet has been expanded to more remote areas and public institutions (schools, hospitals) connected online – prerequisites for AI solutions to work in those locales. One tangible target in healthcare is that by 2030 every region will have deployed at least 12 AI solutions in medical care . This ensures even a rural region will benefit from AI-assisted diagnostics or telemedicine triage. Such a mandate (with progress monitored by the health ministry) effectively forces lagging regions to catch up, backed by federal support if needed. Another initiative is the creation of regional AI centers or pilot zones. Outside the capital, cities like Kazan, Novosibirsk, and Vladivostok have been supported to establish AI innovation hubs or testbeds, where local startups and government agencies collaborate on trials (for instance, an autonomous bus service pilot in a regional city). These hubs are intended to become magnets that keep tech talent in the regions and adapt AI solutions to local languages and contexts.

The inclusion effort also addresses Russia’s diverse population. Multilingual AI tools are being developed for minority languages (such as Tatar, Yakut, Bashkir) so that AI-driven services (like voice assistants or automated translation in courts) are accessible to non-Russian speakers. This mirrors what China has done for ethnic inclusivity , although in Russia the scale is smaller. Still, the principle is to prevent digital inequality where some citizens cannot use AI services due to language or location. The Code of Ethics for AI emphasizes raising awareness about AI ethics throughout society , implying educational outreach not just in big cities but nationwide about the do’s and don’ts of AI – a subtle form of inclusion ensuring everyone is part of the conversation.

In terms of digital access equity, the government promotes projects that deliver AI’s benefits to disadvantaged groups. For example, there are experimental programs using AI for accessibility, like vision-impaired assistance apps developed with Russian AI that narrate surroundings, or AI-driven sign language interpretation for the hearing-impaired on public TV channels. Schools in remote regions are being provided AI-based personalized learning software (adapted to Russian curricula) to help bridge educational gaps. The strategy’s success on inclusion is measured by indicators such as the uptake of AI in public services across all federal subjects, the narrowing of any performance gap between leading regions and lagging ones in digital services, and positive social outcomes like improved healthcare metrics in areas where AI diagnostics are introduced. By mandating and monitoring region-wise AI integration, Russia is striving for a ubiquitous AI rollout that does not leave parts of the country behind, thereby strengthening social cohesion and the overall impact of the AI strategy.

Equitable Benefits & Social Impact

Russia’s AI strategy ultimately frames AI as a tool to advance national development and improve citizens’ quality of life, which brings the question of social equity to the fore. One narrative promoted by officials is that AI can help address long-standing societal challenges in Russia, from uneven healthcare access to labor shortages in remote areas. For instance, AI is being leveraged in public healthcare not only in big-city hospitals but also to extend specialist capabilities to rural clinics via diagnostic algorithms . Early deployments include AI systems that analyze X-ray and MRI images in regions where radiologists are scarce, flagging urgent cases for doctors’ attention. By 2024, well over a billion medical records were digitized and available for AI analysis , and this digital transformation is intended to yield more equitable healthcare outcomes across different regions. The Ministry of Health’s strategy explicitly ties AI to improving accessibility and efficiency of care nationwide . Such initiatives suggest that equity in service provision is a criterion for AI project approval: proposals are evaluated on how they help citizens outside the metropolitan elite.

In education, a similar approach is evident. AI tutors and personalized learning programs (some developed by Russian ed-tech startups) are being piloted in underserved schools, aiming to elevate learning outcomes where teacher shortages or large class sizes persist. The government’s talent development drive, which integrates AI learning into all levels of education, is itself an equalizer – ensuring that a student in a provincial town has a chance to be exposed to AI basics just as one in a top Moscow school would. Over time this can help reduce the urban-rural skill divide. The target of 80% of workers having AI skills implies reaching segments of society that typically lag in tech upskilling, like older workers or those in traditional industries . Programs for continuous education in AI and free certification courses have been introduced to facilitate mid-career training across various regions.

While the government-led efforts are prominent, there is also encouragement of private sector and civil initiatives for social good through AI. The AI Alliance companies often undertake projects with social impact – for example, developing AI systems for environmental monitoring (tracking forest fires or pollution) or AI for road safety – sometimes in partnership with ministries. Such projects tend to be highlighted in press releases to show the human side of AI advancement. Furthermore, Russia is employing experimental legal regimes (sandboxes) to test AI in socially sensitive areas safely . This allows, say, a trial of AI in judicial decision support or welfare benefit allocation under close monitoring, to evaluate benefits and risks before wider rollout. The ethical oversight and feedback from these experiments feed into making sure AI doesn’t inadvertently worsen inequality or harm vulnerable groups.

It is worth noting that Russia’s concept of “social equity” in AI is often intertwined with national unity goals. For example, deploying AI in minority regions is framed as bringing those regions up to par and integrating them more tightly with the nation through technology (which also has a side effect of extending state oversight). Critics might argue that some inclusion rhetoric doubles as justification for surveillance (like facial recognition in public spaces for security, which the state argues benefits society by reducing crime). However, within the strategy documents, the stated intent is to use AI to “address societal issues and improve quality of life” , similar to many other national AI plans. Concrete metrics that Russia might use here include improvements in public service delivery KPIs where AI is applied (such as reduced wait times for medical services, or increased crop yields for farms using AI advisories), and more qualitatively, public satisfaction levels with digital services. If citizens perceive tangible benefits – e.g., faster medical diagnoses, more efficient public transportation due to AI optimization, personalized e-government services – their trust in AI and the government’s AI initiatives is likely to grow.

In summation, Russia’s approach to public trust and social impact in AI is an evolving blend of ethical guidelines, stakeholder engagement, regional empowerment, and targeted social programs. By instilling ethical norms and involving industry in self-regulation, the groundwork for trust is laid. By actively communicating AI’s benefits and integrating AI into everyday services across all regions, the government works to ensure that the average citizen sees AI as a helpful presence in daily life, not a distant or threatening concept. Inclusion and equity are addressed not just as abstract principles but through specific, measurable actions – whether it’s the number of AI-enabled clinics in Siberia or the availability of AI education in village schools. The success of these efforts will be measured in public sentiment and the avoidance of a backlash that some other countries have faced. Thus far, with state-controlled narrative and early ethical initiatives, Russia has managed to promote AI as part of a patriotic and modernizing vision, while acknowledging that maintaining public trust is an ongoing mission requiring transparency, adaptability, and genuine delivery of social value through AI.

2024: Further Expansion of Military AI Capabilities

• Event: In 2024, Russia expands its focus on AI applications in defense and military sectors, prioritizing autonomous weapons, cybersecurity, and AI for intelligence gathering. The government accelerates its investment in AI research for national security, in line with its broader goal of technological self-sufficiency.
• Document/Link: Pending release.

2023: AI for Economic and Industrial Development

• Event: Russia's government continues to prioritize AI applications in economic and industrial sectors, focusing on automation in industries such as energy, agriculture, and manufacturing. The country aims to reduce reliance on foreign technology and ensure domestic technological sovereignty through its AI initiatives.
• Document/Link: Russia’s AI Strategy 2023 (in Russian).

2022: National AI Strategy Progress Report

• Event: A comprehensive Progress Report on Russia's National AI Strategy (adopted in 2019) is released, detailing achievements in AI research, public-private partnerships, and sector-specific AI adoption. The report highlights advancements in AI-driven military technologies, smart cities, healthcare, and industrial automation.
• Document/Link: National AI Strategy Progress Report (2022).

2021: National AI Development and Ethical AI Framework

• Event: In 2021, Russia updates its National AI Strategy, focusing on improving the ethical governance of AI. A new framework for Ethical AI Development is proposed to ensure that AI technologies align with Russia’s political and social values. The framework encourages transparency, fairness, and data sovereignty while addressing the potential risks of AI misuse.
• Document/Link: National AI Development and Ethical AI Framework (2021).

2020: Russian AI Alliance and Private Sector Collaboration

• Event: The formation of the Russian AI Alliance in 2020 brings together leading tech companies, research institutes, and government agencies to collaborate on AI innovations. This alliance promotes AI development in fields such as natural language processing, robotics, and facial recognition technologies. The government also emphasizes AI adoption in critical infrastructure, particularly in energy and transport.
• Document/Link: Russian AI Alliance (2020).

2019: Launch of the National Strategy for the Development of Artificial Intelligence

• Event: Russia unveils its National Strategy for the Development of Artificial Intelligence, which was signed by President Vladimir Putin. The strategy focuses on making Russia a world leader in AI by 2030, with goals to develop AI infrastructure, foster education in AI-related fields, and integrate AI into key sectors like defense, healthcare, and the economy. It also emphasizes AI for national security and self-reliance in technological advancements.
• Document/Link: Russia’s National AI Strategy (2019).

2018: AI in National Security and Defense

• Event: The Russian Ministry of Defense places significant emphasis on AI for defense applications, including autonomous weapon systems, military drones, and AI for strategic decision-making. The government invests heavily in AI research for defense and security, recognizing AI’s transformative potential in warfare.
• Document/Link: Russian Defense AI Applications (2018).

2017: Russia’s AI Research and Development (R&D) Expansion

• Event: In 2017, Russia expands its AI R&D efforts through major investments in state-funded research institutions and collaborations with the private sector. Russia's state-owned tech giant, Rostec, alongside companies like Sberbank and Yandex, are key players in developing AI technologies in fields like natural language processing, facial recognition, and autonomous vehicles.
• Document/Link: Russia AI R&D Expansion (2017).

2016: AI in Smart Cities and Public Administration

• Event: Russia begins integrating AI into smart city initiatives and public administration. Pilot projects for AI-driven urban infrastructure and public service automation are launched in major cities like Moscow and Saint Petersburg. AI technologies are applied to optimize public transport, surveillance, and energy management systems.
• Document/Link: Russia’s Smart Cities Program (2016).

2015: Military Investment in AI Technologies

• Event: Russia ramps up investment in AI for military use, particularly in unmanned aerial vehicles (UAVs) and AI-powered defense systems. The Ministry of Defense begins funding projects focused on AI-driven battlefield strategies, cyber defense, and autonomous systems.
• Document/Link: Russia’s Military AI Investments (2015).

2014: AI in Russia’s Digital Economy Agenda

• Event: AI becomes a central component of Russia’s Digital Economy Initiative, which aims to modernize key sectors through digital transformation. The government begins to allocate resources to support AI development in telecommunications, energy, and finance. This initiative lays the groundwork for Russia’s future AI strategies.
• Document/Link: Russia’s Digital Economy Initiative (2014).