Contents

This report, first published by GINC in December 2024 and updated in January 2026, provides a comprehensive assessment of Russia’s national artificial intelligence strategy across strategic vision, governance, policy instruments, national capability, research and talent, regulation, deployment, and global engagement. It also traces the evolution of Russia’s AI strategy from its early foundations prior to 2015 through successive phases of planning, scaling, and operationalisation to the present.

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

Part 2: National Capability, Hard Power & Artificial Intelligence
2A. Critical Technologies
2B. Strategic Infrastructure
2C. National Security

Part 3: Research, Innovation and Talent Development
3A. Research Performance
3B. Innovation Ecosystem
3C. Talent, Education & Mobility

Part 4: Regulatory Architecture, Deployment Pathways & Global Influence
4A. Regulatory, Ethical & Safety Frameworks
4B. Industrial Deployment & Tech Diffusion
4C. International Engagement & Standards

Part 5: Performance, Resilience and Social Impact
5A. Performance Metrics & Monitoring
5B. Strategic Foresight & Resilience
5C. Public Trust, Inclusion & Social Equity

Part 6: Evolution of Russia's National AI Strategy (2015–2025)
6A. 2024–2025. Sanctions-Era Execution, Sovereignty & Strategic Repositioning
6B. 2020–2023. State-Led Scaling, Champion Firms & Governance Consolidation
6C. 2015–2019. National Strategy Formation & Capability Mobilisation
6D. Pre-2015. Scientific Foundations, Digital State Building & Early AI Research

1. National Vision and Strategic Foundations

Russia’s National AI Strategy is shaped by a core objective of technological sovereignty, alongside goals of economic modernisation and national security enhancement under conditions of sustained external constraint. First adopted in 2019 and updated in 2023, the strategy sets out a long-term ambition to position Russia among the world’s leading AI powers by 2030, while increasingly emphasising import substitution, resilience, and state control over critical technologies.

AI is framed both as a lever for domestic productivity and public-sector reform and as a strategic domain of geopolitical competition, driving continued government support for research, workforce development, and priority industrial applications. The strategy rests on a predominantly state-led model that coordinates public institutions, state-owned enterprises, and selected private actors, guided by principles of sovereignty, security, and regulated deployment. This section examines Russia’s strategic objectives, the governance structures used to implement them, and the policy instruments mobilised to advance the national AI agenda.

1A. Strategic Vision & Objectives

Russia’s leadership continues to frame artificial intelligence as a strategic frontier central to economic resilience, state power, and national security. The National AI Strategy, approved by presidential decree and updated most recently in 2023, defines AI as a tool to improve quality of life, strengthen sovereignty, and enhance the competitiveness of the Russian economy under conditions of long-term geopolitical rivalry and sanctions pressure (Kremlin Decree No. 490, 2019; Government of the Russian Federation, 2023 update). The strategy reiterates the ambition for Russia to secure leading positions in selected AI domains by 2030, while explicitly prioritising self-reliance, security, and alignment with national values. This section outlines how AI is framed in geopolitical terms, details Russia’s strategic objectives and timelines, and situates technological sovereignty at the core of AI development.

AI as a Geopolitical and Economic Priority

Russian officials consistently characterise AI as a decisive arena of global competition in which strategic lag would translate directly into economic and military vulnerability. In a widely cited 2017 address, Vladimir Putin stated that leadership in AI would confer unprecedented global power, a message that has since been echoed in strategic documents and senior-level speeches (RIA Novosti, 2017). Subsequent policy statements liken AI competition to a long-term technological race with direct implications for defence, productivity, and international influence.

The 2019 National AI Strategy positions AI alongside space and nuclear technologies as a pillar of modern national power, arguing that digital and algorithmic sovereignty is indispensable in the 21st century (Kremlin, 2019). This framing justifies sustained state intervention, with AI treated not merely as a commercial technology but as critical infrastructure underpinning economic modernisation, military effectiveness, and state capacity. By January 2026, this geopolitical framing has hardened further, with official commentary increasingly linking AI progress to resilience under sanctions and reduced exposure to foreign technology dependencies (Valdai Discussion Club, 2024).

National Goals and 2030 Leadership Ambitions

Russia’s AI strategy establishes 2030 as the principal horizon for achieving global relevance, while recognising near-term milestones as stepping stones rather than endpoints. Earlier targets for 2024, focused on improving international standing and scaling deployment, are now largely interpreted as partial or unevenly met, prompting a stronger emphasis on consolidation and selective excellence (Accounts Chamber of the Russian Federation, 2024). By 2030, the strategy aims to eliminate critical gaps with advanced economies and to achieve leadership in specific AI niches aligned with national strengths, such as applied computer vision, speech technologies, industrial AI, and defence-related applications.

Socio-economic objectives remain central. Official documents highlight AI’s role in raising productivity, automating routine administrative processes, and improving service delivery in healthcare, education, transport, and agriculture (Ministry of Economic Development, 2023). Quantitative targets remain indicative rather than binding, but government projections continue to cite growth in the domestic AI market to tens of billions of rubles annually by the end of the decade, alongside a fivefold expansion in AI-trained graduates compared with early-2020s levels (Government Analytical Centre, 2024). These goals reflect a strategic preference for a sustainable, domestically anchored AI ecosystem rather than dependence on imported platforms or models.

Technological Sovereignty and Value Framework

Technological sovereignty has become the defining pillar of Russia’s AI vision. The 2023 update to the National AI Strategy explicitly references “unilateral restrictive measures” and technology embargoes as structural challenges shaping policy choices (Government of the Russian Federation, 2023). In response, Russia prioritises domestic development of AI-relevant hardware, cloud infrastructure, and software stacks, alongside sovereign data resources and Russian-language AI systems. Policy emphasis has shifted toward ensuring continuity of critical AI functions in government, defence, and key industries regardless of external constraints (Ministry of Digital Development, 2024).

Alongside sovereignty, the strategy articulates a national value framework for AI deployment. Official principles stress legality, security, transparency, and human oversight, with AI systems expected to comply with Russian constitutional norms and data protection rules (Roskomnadzor, 2023). An ethical AI framework introduced in 2021 and reinforced in subsequent guidance promotes fairness, explainability, and data sovereignty, positioning these concepts as safeguards against both social harm and foreign influence (Alliance for AI Ethics, Russia, 2021). Public trust is treated as a measurable strategic variable: government targets aim to raise public confidence in AI technologies toward 80 percent by 2030, recognising legitimacy and acceptance as prerequisites for large-scale deployment (Government Analytical Centre, 2024).

Taken together, Russia’s strategic vision for AI as of January 2026 is defined less by aspirations to dominate global AI markets than by a determination to secure autonomous capability, preserve state control over critical technologies, and deploy AI in ways consistent with national security priorities and domestic values amid prolonged strategic competition.

1B. Governance Architecture

Russia’s AI governance remains highly centralised in strategic direction and distributed in execution, combining presidential-level signalling with implementation through federal ministries, state corporations, and a small set of “national champions.” The National AI Strategy was approved by Presidential Decree No. 490 (10 Oct 2019), which also tasked the government with embedding AI into national programmes and reporting progress annually, anchoring AI policy in the top tier of the state planning system (Decree No. 490, 2019). In February 2024, President Putin signed Decree No. 124, widely described as a substantial update to the strategy, adding new definitions, implementation mechanisms, and an explicit focus on execution under sanctions and technology-access constraints (TASS, 16 Feb 2024; Regulations.ai summary; AEB Digital Law Digest, 2024). By January 2026, this governance framework has increasingly emphasised long-term programme delivery, sovereignty, and resilience, with AI positioned inside broader state digital transformation and “data economy” agendas (Government of Russia, 21 Nov 2023).

Presidential Oversight and Strategic Coordination

Strategic oversight is exercised through the President and federal government, with national objectives translated into instructions, decrees, and national project delivery mechanisms. Following successive “Artificial Intelligence Journey” conferences, the Kremlin issued implementation instructions calling for the creation of a dedicated Artificial Intelligence federal project within the forthcoming “Data Economy” national project, signalling tighter integration of AI into Russia’s core economic planning architecture (Kremlin, 17 Jan 2024). The government has since framed the “data economy” as a decade-long national priority, reinforcing AI’s status as a cross-cutting capability embedded across public administration, industry, and infrastructure (Government of Russia, 21 Nov 2023; Interfax, 11 Oct 2023).

Operational coordination typically falls within the Deputy Prime Minister portfolio responsible for digital development, while line ministries retain authority over sectoral policy instruments and implementation. This produces a governance model in which strategic intent is set at the centre, but execution is managed through ministerial portfolios, state corporations, and national projects, with progress monitored through Cabinet-level reporting and oversight mechanisms.

Public–Private Coordination and State-Aligned Champions

A defining feature of Russia’s AI governance is the institutionalised role of major domestic firms in both policy consultation and delivery capacity. The Alliance in the Sphere of Artificial Intelligence (AI Alliance) functions as a formal coordination platform linking the government with leading technology and industrial actors, including Yandex, Sber, VK, Gazprom Neft, MTS, and RDIF (AI Alliance; TASS, 8 Nov 2019). The Alliance supports policy feedback, standard-setting, and the identification of priority deployment areas, effectively embedding industry expertise within the governance apparatus.

This model reflects a deliberate reliance on a small number of high-capacity, state-aligned “champions” to scale AI across the economy. Firms such as Sber and Yandex operate supercomputing infrastructure, large-scale data platforms, and applied AI labs that function as quasi-national assets, while defence- and industry-oriented state corporations pursue aligned AI programmes in manufacturing, energy, and security domains (DGAP, 2022). As at January 2026, this concentration has deepened rather than diversified, reinforcing a governance approach that favours control, scale, and execution certainty over ecosystem breadth.

Interagency and Institutional Implementation Mechanisms

Russia implements AI policy through a network of ministries, agencies, and legal instruments rather than a single central AI authority. The Ministry of Digital Development (MinTsifry) plays a central role in digital infrastructure, regulatory enablement, and state digital transformation, while the Ministry of Economic Development coordinates economic and programme-level aspects of AI integration within national projects (Government of Russia, 21 Nov 2023). Sector ministries lead domain-specific deployment, including healthcare, education, transport, and defence-related applications.

A key execution tool is the use of experimental legal regimes (regulatory sandboxes), which allow controlled testing of AI-enabled services under tailored compliance conditions. These regimes were enabled nationally through Federal Law No. 123-FZ, initially piloted in Moscow and subsequently referenced as a mechanism for scaling AI and data-driven services (Gowling WLG overview, 2020; CIS Legislation). Sandboxes operate as a governance valve, enabling innovation and interagency coordination while maintaining central oversight.

Across ministries and institutions, governance relies on regular reporting, roadmap reviews, and feedback loops embedded in national projects. Progress assessments conducted in 2022–2024 informed the 2024 strategic amendments, particularly in areas such as computing capacity, data availability, and skills bottlenecks (AEB Digital Law Digest, 2024). Overall, Russia’s AI governance architecture blends presidential control, programme-based execution, regulatory experimentation, and champion-led delivery, forming a system designed to sustain AI development under long-term strategic and technological constraints.

1C. Policy Instruments & Incentives

As of January 2026, Russia’s AI strategy is implemented through a tightly coordinated set of policy instruments designed to sustain development and deployment under fiscal pressure, sanctions, and technology-access constraints. The approach remains predominantly state-driven, combining targeted public investment, regulatory flexibility, and capacity-building measures with mandated or incentivised participation from large, state-aligned firms. Rather than relying on open market dynamics, authorities use national programmes, procurement, and legal engineering to concentrate resources on priority AI domains while shaping the conditions under which AI can be developed, tested, and scaled domestically (Government of the Russian Federation, 2023).

Public Investment and National Programmes

Public funding continues to serve as the backbone of Russia’s AI policy, albeit at a more constrained and selective scale than originally envisaged before 2022. The National AI Strategy is implemented through a federal roadmap integrated into national digital programmes, initially under the “Digital Economy” framework and increasingly aligned with the forthcoming “Data Economy” national project (Kremlin, 17 Jan 2024). Following post-2022 revisions, official planning documents indicate total state budget spending on AI of approximately 24–25 billion rubles between 2023 and 2030, with funds directed toward priority research, applied pilots, and enabling infrastructure rather than broad-based subsidies (AEB Digital Law Digest, 2024).

This public funding is explicitly designed to leverage significantly larger volumes of extrabudgetary investment. Major state-aligned firms, particularly Sber, Yandex, Gazprom Neft, MTS, and Rostec-linked entities, have committed to long-term AI investment programmes aligned with national priorities. Sber has publicly disclosed plans to invest tens of billions of rubles annually in AI development through 2030, positioning corporate balance sheets rather than the federal budget as the primary financial engine of AI scale-up (Sber annual disclosures). The government reinforces this model through procurement mandates and pilot requirements, ensuring domestic AI solutions are adopted by state agencies and state-owned enterprises, especially in healthcare, transport, energy, and public administration (Ministry of Health, Russia).

Investment priorities remain mission-oriented. Official roadmaps continue to highlight natural language processing, computer vision, intelligent decision-support systems, and core AI methods as focus areas, with funding conditioned on demonstrable progression from research to deployment (Government Analytical Centre, 2024). Flagship infrastructure investments, including national supercomputing capacity hosted by state-aligned firms, are treated as shared strategic assets supporting both civilian and security-related applications.

Regulatory Sandboxes and Legal Frameworks

Regulatory flexibility is a central policy instrument in Russia’s AI strategy. The legal foundation for experimentation is provided by Federal Law No. 123-FZ “On Experimental Legal Regimes in Digital Innovation,” which enables time-limited regulatory sandboxes for AI and data-driven technologies (CIS Legislation). The most prominent implementation remains the Moscow AI experimental zone, launched in July 2020 for a five-year term, allowing testing of AI systems in urban services, transport, and public administration under tailored compliance rules (Moscow Government overview).

By 2025–2026, sandbox mechanisms have expanded beyond smart-city use cases to include autonomous transport, telemedicine AI, and decision-support systems, serving as a pathway to national rollout once legal and technical risks are assessed. In parallel, Russia has continued to adapt its legal framework to clarify responsibility and liability for AI-enabled decisions. Legislative amendments adopted by the State Duma in 2024–2025 address civil liability and insurance mechanisms for harm caused by AI systems, signalling a shift from pure experimentation toward normalised deployment (State Duma legislative tracker).

Data governance remains restrictive but instrumental. Amendments to personal data legislation emphasise consent, anonymisation, and localisation requirements, reinforcing sovereign control over datasets used for AI training while allowing limited flexibility for approved use cases (Roskomnadzor). At the same time, authorities promote state-controlled open data initiatives and sectoral data sharing to mitigate data shortages for domestic AI developers, particularly in healthcare, transport, and industry.

Standards, Ethics, and Certification

Standards and soft law instruments complement formal regulation. Russia continues to develop national GOST standards for AI systems, covering software quality, lifecycle management, and reliability, with Rosstandart and industry-led technical committees playing coordinating roles (Rosstandart). These standards are designed to ensure baseline quality and to enable certification of domestic AI products for procurement and export to aligned markets.

Ethical governance is addressed through voluntary but politically endorsed instruments. The Code of Ethics for Artificial Intelligence, adopted in 2021 and reaffirmed in subsequent strategy updates, outlines principles such as non-discrimination, transparency, human oversight, and data sovereignty (Alliance for AI Ethics). While not legally binding, these principles are increasingly referenced in procurement criteria and public-sector deployment guidelines, embedding them indirectly into implementation practice.

Industry Collaboration and Capacity-Building Incentives

Collaboration and capacity-building are incentivised through a mix of fiscal, institutional, and reputational tools. The Alliance in the Sphere of Artificial Intelligence functions as a coordination hub for joint projects, shared datasets, and policy feedback between government and major firms (AI Alliance). Member companies collaborate on open-source tools, Russian-language AI models, and sector-specific applications, often supported by co-financing arrangements or preferential access to pilot programmes.

The government extends IT-sector incentives to AI developers, including reduced corporate tax rates, social contribution relief, and access to concessional loans, building on measures introduced for the broader digital sector after 2020 (Ministry of Digital Development). Innovation hubs such as Skolkovo continue to provide tax exemptions, grants, and infrastructure for AI startups and research teams (Skolkovo Foundation).

Human capital development remains central. Federal programmes mandate the expansion of AI-related education across secondary, vocational, and higher education, with increased admissions quotas, scholarships, and industry-linked curricula aimed at reaching over 15,000 AI-specialised graduates annually by 2030 (Ministry of Education, Russia). Large-scale reskilling initiatives target the existing workforce through online platforms and employer-linked training schemes. To mitigate talent loss, the government offers grants, relocation incentives, and simplified visa pathways for foreign specialists, particularly from neighbouring and partner countries, while encouraging Russian researchers to remain embedded in domestic institutions (Government Analytical Centre, 2024).

Taken together, these instruments form a highly interventionist and selectively funded policy mix that prioritises execution certainty, sovereign control, and alignment with national strategic objectives, using the state’s convening power to mobilise industry, academia, and talent in pursuit of Russia’s long-term AI goals.

Part 2: National Capability, Hard Power & Artificial Intelligence

2A. Critical Technologies

Building and sustaining foundational AI capabilities sits at the core of Russia’s national AI strategy. From the outset, AI has been framed as a matter of technological sovereignty and national security, with the explicit objective of reducing critical dependencies on foreign hardware, data platforms, and software ecosystems. Presidential Decree No. 490 (2019) established this orientation, identifying compute infrastructure, data availability, core algorithms, and domestic software tools as priority foundations for long-term AI development (Kremlin, Decree No. 490). Subsequent strategy updates reinforced this focus, particularly in response to sanctions, by tightening the emphasis on self-reliant capability building and resilience across the full AI stack (Government of Russia, AI Strategy amendments; TASS, 16 Feb 2024).

Compute Infrastructure and Supercomputing

High-performance computing is recognised as the most binding constraint on Russia’s AI ambitions. The National AI Strategy explicitly tracks domestic supercomputing capacity as a performance indicator, reflecting the central role of compute in training and deploying advanced models (Government of Russia, AI Strategy update). Since 2019, Russia’s largest technology firms and research institutions have driven incremental expansion of AI-focused HPC infrastructure.

Sber launched the Christofari AI supercomputer in 2019, followed by Christofari Neo in 2021, designed specifically for deep learning workloads (Data Center Dynamics). Yandex subsequently deployed larger clusters, including the “Chervonenkis” system, which entered the global TOP500 ranking in 2021 (TOP500, Nov 2021). These systems underpin domestic foundation-model development and applied AI research, though Russia’s aggregate frontier compute remains well below that of the United States and China.

Export controls on advanced GPUs have reshaped compute strategy. Official statements acknowledge persistent hardware constraints and prioritise efficient utilisation, clustering of existing resources, and subsidised access to cloud compute for researchers and startups (Reuters, 19 Nov 2025). Parallel R&D programmes target domestic AI accelerators, neuromorphic processors, and adaptation of locally designed CPUs for AI workloads, although these efforts remain at early stages (Ministry of Industry and Trade). The emphasis is on maintaining continuity of AI development rather than achieving immediate parity with global leaders.

Data Ecosystems and Access

Data availability and governance constitute the second foundational pillar. Russia’s strategy seeks to transform large volumes of administrative, industrial, and scientific data into usable AI training resources while maintaining state control. Long-standing data localisation requirements ensure that personal data on Russian citizens is stored domestically, reinforcing sovereign control over critical datasets (DLA Piper, Russia data localisation).

The government has prioritised centralisation and interoperability of public-sector data. Integrated national platforms have expanded in healthcare, social services, transport, and taxation, enabling AI development at scale. In healthcare alone, authorities report the consolidation of over one billion electronic medical records into unified systems supporting clinical AI applications (Ministry of Health, Russia). Similar integration efforts apply to satellite imagery, urban infrastructure data, and agricultural monitoring.

To support developers, the strategy promotes data repositories, open datasets, and sectoral data hubs, complemented by national standards to ensure interoperability. Russia has introduced dozens of AI-related standards in healthcare and other domains to harmonise data formats and support cross-regional model training (Rosstandart). Recognising persistent quality gaps, government-backed programmes fund data cleaning, annotation, and labelling, including large-scale efforts to create high-quality Russian-language corpora for NLP and computer vision. These initiatives aim to convert Russia’s scale advantage into a competitive AI asset.

Software Ecosystem and Open-Source Tools

Software sovereignty is treated as equally critical. Policy documents emphasise the development of domestic AI frameworks, libraries, and platforms, supported by preferential procurement and grant funding (Ministry of Digital Development). Open-source development plays a central role: tools such as CatBoost (Yandex) and DeepPavlov have become widely used within Russia, while Sber and other firms have released NLP and AutoML libraries to reduce reliance on foreign toolchains.

While global frameworks such as PyTorch and TensorFlow remain in use, authorities actively encourage Russian-maintained alternatives and repositories to mitigate platform risk. Public-sector deployments increasingly prioritise domestically developed AI software, and registries of approved AI solutions catalogue reusable codebases for government and industry adoption (Government of Russia). Success is measured through adoption rates, reductions in foreign API usage, and the integration of domestic software across regulated sectors.

Cloud platforms form part of this software foundation. SberCloud, Yandex Cloud, and VK Cloud provide AI-as-a-service, APIs, and development sandboxes hosted within Russia, ensuring compliance with localisation and security requirements while lowering barriers for smaller firms and regional authorities (SberCloud; Yandex Cloud).

Emerging AI Models and Applications

The maturation of compute, data, and software foundations has enabled Russia to develop indigenous large-scale AI models, particularly in generative AI. By 2023–2024, Russia joined a small group of countries producing domestic foundation models. Sber’s GigaChat and YandexGPT (YaLM) are trained primarily on Russian-language data and designed for local cultural and regulatory contexts (Reuters on GigaChat; YandexGPT). These models are deployed across banking, public services, and consumer platforms, and are increasingly treated as strategic national assets.

Foundational AI work also feeds directly into priority applications aligned with national interests, including healthcare diagnostics, smart-city systems, logistics optimisation, energy infrastructure monitoring, and security-related computer vision. Federal programmes mandate the rollout of certified AI systems once foundational readiness is achieved, accelerating diffusion from research into practice (Ministry of Health AI programme).

Export potential is increasingly cited as a validation metric. Russian firms market AI products such as facial recognition and cybersecurity tools abroad, particularly in non-Western markets, framing exportability as evidence of foundational strength (OECD AI Policy Observatory – Russia).

Across compute, data, software, and models, Russia’s foundational AI strategy prioritises self-reliance, continuity, and controlled scaling. While gaps with global leaders remain, sustained investment and coordinated policy have produced a functional domestic AI stack capable of supporting national needs and adapting to new technological waves under constrained conditions.

2B. Strategic Infrastructure

Data Governance and Sovereignty

Russia’s AI strategy treats data as a strategic national resource and increasingly frames data control as an element of sovereignty and security. A foundational pillar is Russia’s data localisation regime, introduced via amendments commonly referenced as Federal Law No. 242-FZ (to the personal data law), which has required since 2015 that personal data of Russian citizens be initially recorded and stored in databases located in Russia (with limited pathways for subsequent cross-border transfer) (overview; summary of the localisation rule). This localisation logic has expanded in practice toward broader “sovereign stack” ambitions, including domestically operated platforms and state-aligned infrastructure for identity, finance, and public services.

Alongside hard law, Russia has leaned on “soft regulation” to build legitimacy for large-scale data use. The Code of Ethics for Artificial Intelligence (adopted in 2021) remains the flagship voluntary framework, emphasising transparency, privacy, accountability, and human oversight. By 2024, reporting indicates 423 organisations had signed the code, signalling broad institutional adoption across industry and public entities (DataGuidance, 2024; AI Ethics Code site).

State-backed consolidation of sensitive datasets has also advanced. The Unified Biometric System (UBS), used for remote identity verification (notably in banking and digital services), has been treated as national digital infrastructure and has drawn attention for expanding central control over biometric data (Rostelecom on UBS; Human Rights Watch analysis). In parallel, sectoral digitisation in healthcare and other domains continues to create larger administrative datasets available for approved AI deployment pathways, even as governance remains restrictive and security-oriented.

From late 2024 through January 2026, Russian leadership also sharpened its stance against dependence on foreign platforms and models. At the AI Journey event in November 2025, President Putin called for a national task force for domestic generative AI, explicitly arguing that reliance on foreign large language models is unacceptable due to sovereignty and information-influence concerns, and linking AI growth to domestic data centres and supporting energy infrastructure (Reuters, 19 Nov 2025).

Computing Capacity and High-Performance Infrastructure

Compute availability remains one of the most binding constraints in Russia’s AI ambitions, and official messaging since 2023 has increasingly acknowledged “capacity” gaps under sanctions conditions. This is now reinforced at the political level: Putin’s November 2025 remarks tied Russia’s generative AI ambitions to building new data centres and nearby energy supply, including small-scale nuclear power solutions for reliable power (Reuters, 19 Nov 2025).

Russia’s most visible high-performance AI infrastructure has been built by its largest tech firms. Sber’s Christofari (2019) and Christofari Neo (2021) were designed explicitly for AI workloads; Christofari Neo’s published performance is approximately 11.95 PFLOPs (FP64) (ICT Moscow profile; Data Center Dynamics, 2021).

Yandex then scaled to larger training clusters. Its “Chervonenkis” supercomputer reached ~21.53 PFLOPs and ranked 19th on the TOP500 list (Nov 2021), illustrating the concentration of AI training capacity among a handful of national champions (TOP500 Nov 2021; Yandex supercomputers). These systems underpin domestic foundation-model efforts, including Sber’s and Yandex’s LLM families, but Russia’s overall frontier compute remains materially constrained relative to the US and China, especially under tightened access to advanced GPUs and semiconductor supply chains—an issue repeatedly noted in international reporting on Russia’s AI development environment (Reuters, 19 Nov 2025).

Domestic Cloud and Digital Platforms

With Western hyperscalers largely unavailable or constrained, Russia has doubled down on domestic cloud platforms as both a sovereignty instrument and an adoption accelerator. SberCloud and Yandex Cloud provide infrastructure and AI services hosted on Russian soil, enabling regulated sectors (finance, government, critical infrastructure) to deploy AI while meeting localisation and security requirements. These platforms also provide “AI-as-a-service” offerings that reduce barriers for smaller firms and regional governments.

The domestic platform strategy also includes efforts to reduce dependence on foreign software infrastructure. Yandex’s decision to open-source its big-data storage and processing platform YTsaurus in 2023 reflects a move toward domestically governed tooling that can support large-scale data workloads inside Russia’s ecosystem (Yandex announcement, 20 Mar 2023; YTsaurus project site).

Institutionally, Russia has also signalled interest in national-scale shared infrastructure for data processing and reuse. The post-2023 shift toward a broader “data economy” architecture implies that AI deployment will increasingly be coupled to shared state-supported data platforms and standardised service components rather than bespoke deployments city by city (Government of Russia, “Data Economy” framing).

Connectivity and National Digital Infrastructure

AI diffusion depends on baseline connectivity: broadband reach, mobile coverage, and resilient networks. Russia’s Digital Economy programme (implemented as a national project through 2024) invested heavily in connectivity expansion, including broadband links to public institutions and rural areas, creating the substrate for digital public services, IoT deployments, and AI-enabled delivery in remote regions (background on the national project).

Progress on 5G has been slower and more uneven than early targets, reflecting equipment constraints and localisation requirements. Reporting indicates Russia planned pilot 5G zones using domestically built base stations in 2025 (Data Center Dynamics, 2023), while other analyses note that broader rollout has faced significant delays and policy friction through 2024–2025 (intellinews, 2024; TAdviser 5G development overview).

Network resilience and sovereign-control measures also intersect with AI infrastructure. Russia’s “sovereign Runet” framework and periodic tests of the ability to operate under external disruption are frequently justified on security and continuity grounds; Reuters reported on large-scale Runet tests in 2021 aimed at ensuring the Russian segment of the internet can function under external threats (Reuters, 22 Jul 2021; Clifford Chance explainer).

Finally, Russia’s “smart city” and surveillance infrastructure illustrates how connectivity and data availability translate into AI deployment capacity. Public reporting has cited very large camera networks and significant facial recognition integration, with the Minister for Digital Development cited as saying Russia has over 1 million video surveillance cameras and that a substantial fraction are connected to facial recognition, with ~230,000 in Moscow (Eurasianet, 2024). This infrastructure provides abundant domestic training and inference environments for computer vision, while also reinforcing the state’s preference for sovereign, centrally governed data and deployment pathways.

Taken together, Russia’s January 2026 posture is a “full-stack sovereignty” trajectory: localise and secure data; expand domestic compute and data centres; anchor deployment on Russian cloud platforms and open tooling; and continue upgrading nationwide connectivity and resilience mechanisms under long-term external constraint. This aligns with the state’s broader objective that AI should contribute over 11 trillion rubles to GDP by 2030, a figure repeatedly cited in official and media reporting, including at AI Journey 2025 (Reuters, 19 Nov 2025; Sber, AI Journey coverage).

2C. National Security

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 3: Research, Innovation and Talent Development

3A. Research Performance

3B. R&D & Innovation Ecosystem

Russia’s AI innovation system is built around concentrated state direction, a small number of industrial champions, and targeted public investment rather than broad market experimentation. By January 2026, this model has delivered scale, coordination, and resilience in priority AI domains, but it also embeds structural trade-offs, particularly in startup dynamism and global integration. The effectiveness of Russia’s national AI strategy therefore hinges on whether this tightly managed R&D ecosystem can continue to generate breakthrough innovation and commercially viable technologies under sustained technological and geopolitical constraints.

Strategic R&D Priorities and National Programmes

Russia’s research and innovation approach to artificial intelligence is shaped by long-term strategic planning combined with selective, mission-oriented funding. The National Strategy for the Development of Artificial Intelligence (2019) set the overarching objective of positioning Russia among the global leaders in AI by 2030, with a strong emphasis on technological self-sufficiency, applied deployment, and economic impact (Presidential Decree No. 490, 2019). The strategy defined interim milestones for 2024 and a final horizon for 2030, prioritising growth in the domestic AI research base, expansion of applied AI in industry, and the emergence of export-capable AI solutions in sectors such as manufacturing, energy, agriculture, and public administration.

Implementation was channelled through a dedicated federal project “Artificial Intelligence”, originally embedded in the national Digital Economy programme and now increasingly aligned with the forthcoming Data Economy national project (Government of Russia). Between 2021 and 2024, official disclosures indicate total funding of approximately ₽31–32 billion, including around ₽27 billion in federal budget allocations, supporting AI R&D grants, data platforms, supercomputing capacity, and applied pilots (AEB Digital Law Digest, 2024). In 2025, budget allocations for AI-related federal programmes were maintained at elevated levels relative to pre-2020 baselines, reinforcing AI’s status as a protected strategic priority despite broader fiscal pressures.

A major inflection point came with the 2023–2024 update to the National AI Strategy, enacted via presidential decree, which explicitly acknowledged new structural constraints, including limited access to advanced hardware, insufficient domestic AI solutions, and persistent skills shortages under sanctions conditions (TASS, 16 Feb 2024; Regulations.ai summary). The updated strategy reaffirmed federal support for AI research and innovation while tightening the focus on applied outcomes, resilience, and domestic capability.

Public–Private Alliances and Industry Leadership

A defining characteristic of Russia’s AI innovation ecosystem is the central role of large, state-aligned corporations operating within a formalised public–private framework. Shortly after the adoption of the national strategy, the Alliance in the Sphere of Artificial Intelligence was established in 2019 by major firms including Sber, Yandex, VK (formerly Mail.ru Group), MTS, Gazprom Neft, and the Russian Direct Investment Fund (AI Alliance; TASS, 8 Nov 2019). The Alliance’s stated purpose is to coordinate business, academic, and government efforts in support of national AI objectives.

In practice, this alliance-centric model has made large firms the primary engines of AI innovation, in contrast to startup-dominated ecosystems elsewhere. Alliance initiatives include shared data repositories, joint R&D projects, standard-setting activities, and venture platforms linking startups with corporate accelerators and investors. Programmes such as AI-Hub aim to integrate early-stage firms into corporate value chains rather than scaling independently (AI Alliance – AI Hub).

Each major participant contributes domain-specific strengths: Sber provides financial resources, AI research labs, and national-scale computing infrastructure; Yandex contributes expertise in search, language technologies, autonomous systems, and cloud platforms; Gazprom Neft and MTS drive applied AI in energy, telecoms, and industrial analytics. Defence-oriented conglomerates such as Rostec run parallel AI R&D programmes aligned with national security priorities (DGAP, Russia’s Digital Sovereignty). This model ensures scale and coordination but concentrates innovation capacity among a relatively small number of actors.

Research Centres and Breakthrough Innovation Programmes

To strengthen foundational research and reduce reliance on imported intellectual property, the government has invested heavily in AI research centres of excellence. Beginning in 2021, competitive grant programmes established multiple waves of centres focused on core AI methods and sectoral applications. A third major funding round announced in 2023 awarded ₽4.7 billion to seven leading institutions, each receiving ₽676 million over 2024–2025, including HSE University, ITMO University, MIPT, Skoltech, Innopolis University, ISP RAS, and Moscow State University (Ministry of Science and Higher Education).

These centres are mandated to conduct fundamental research in areas such as trustworthy AI, multi-agent systems, and advanced learning architectures, while also delivering applied solutions in healthcare, urban management, industry, and public services. Government statements indicate that centres established in earlier funding waves account for a significant share of Russia’s peer-reviewed AI publications, underscoring their role in consolidating research output (Government Analytical Centre).

The strategy sets explicit publication and impact targets, including ambitions to substantially increase Russia’s output in top-tier AI conferences and journals by 2030. Parallel efforts are underway to align academic research with national priorities through a more unified AI research agenda coordinated by the Ministry of Science and the Ministry of Economic Development. Defence and security-related AI R&D continues to receive separate funding streams through the Ministry of Defence and state corporations, particularly in autonomous systems, robotics, and decision-support technologies (TASS, defence AI coverage).

Startup Support and Innovation Funding

Russia’s AI startup ecosystem remains smaller and more state-dependent than those of leading Western or East Asian economies, but targeted interventions aim to improve commercialisation pathways. The Foundation for Assistance to Small Innovative Enterprises (FASIE) operates dedicated grant programmes such as “Start AI”, providing early-stage teams with grants of up to ₽5 million to develop prototypes and business models (FASIE). Official reporting suggests that these grants have generated measurable downstream effects in revenue and employment among recipient firms.

In 2024–2025, additional initiatives under programmes such as “Domestic Solutions” were introduced to accelerate the adoption of Russian-developed AI products across sectors including manufacturing, healthcare, education, and logistics (Government of Russia). These schemes complement corporate venture activity by Sber, MTS, and RDIF, as well as accelerator programmes hosted at Skolkovo and Innopolis, which offer tax incentives, infrastructure, and access to pilot customers (Skolkovo Foundation; Innopolis).

By the mid-2020s, official estimates suggested that around 1,000 Russian companies were actively developing or deploying AI solutions, with financial services, telecoms, and public-sector applications leading adoption (MinTsifry). While sanctions and reduced access to foreign capital continue to constrain scale and internationalisation, the government’s innovation policy explicitly seeks to balance its champion-led model with a broader base of startups and SMEs capable of contributing to national AI objectives.

Overall, as of January 2026, Russia’s R&D and innovation ecosystem for AI is characterised by strong state direction, concentrated industrial leadership, and targeted research investment, with incremental efforts to expand startup participation and commercialisation under conditions of sustained external constraint.

3C. Talent, Education & Mobility

Human capital has emerged as one of the most decisive constraints and enablers of Russia’s national AI strategy. Faced with external technology restrictions and persistent talent outflows, the state has prioritised early AI education, accelerated university training, and large-scale workforce upskilling to secure a self-sustaining domestic talent base. By January 2026, progress is evident in the scale of training capacity and institutional coordination, but the durability of Russia’s AI ambitions increasingly depends on its ability to retain skilled professionals and convert educational throughput into long-term capability.

STEM Curriculum Integration and Youth Initiatives

By January 2026, Russia has further embedded artificial intelligence, programming, and data skills into general and secondary education as part of a long-term talent pipeline strategy. Building on pilots launched in the early 2020s, AI concepts are now introduced through a mix of compulsory informatics modules, electives, extracurricular clubs, and competition-based learning. The Ministry of Education and the Ministry of Digital Development frame AI literacy as a foundational skill alongside mathematics and computer science, consistent with the National AI Strategy’s emphasis on early talent identification (Ministry of Education, Russia; MinTsifry).

A flagship initiative remains the school-level AI education programme supported by Sber and the Alliance in the Sphere of Artificial Intelligence, which since 2021 has equipped hundreds of schools with simulators, teaching modules, and cloud-based environments for basic machine learning experiments (AI Alliance; Sber AI education). Students are exposed to datasets, simple model training, and applied tasks in areas such as image recognition and forecasting, lowering the barrier to entry for AI learning at an early age.

Elite youth development is anchored by the Sirius Educational Center in Sochi, which continues to host intensive AI, robotics, and data science programmes for gifted secondary school students from across the country (Sirius Center). In parallel, specialised schools and lyceums in Moscow, Kazan, St. Petersburg, and Novosibirsk offer AI-focused tracks, often in partnership with universities and technology firms.

Competitive pathways remain a central mobilisation tool. Russia continues to host and sponsor national Olympiads in informatics and AI, while also supporting international initiatives such as the AI International Junior Contest, launched in 2021 and positioned as a global platform for identifying young AI talent (AI Junior Contest). These mechanisms provide accelerated university admission, scholarships, and early access to research labs for top performers. Teacher capacity is addressed through federal retraining programmes that upskill mathematics and informatics teachers in programming, data analysis, and AI fundamentals (Ministry of Education professional development programmes). Despite progress, official assessments acknowledge persistent regional disparities in AI education resources, prompting efforts by the Government’s Analytical Center to scale successful regional models nationwide (Government Analytical Centre).

Expansion of AI Education in Universities

Higher education remains the core pillar of Russia’s AI talent strategy. Between 2021 and 2025, the federal government funded the creation and expansion of over 120 AI-focused bachelor’s and master’s programmes across leading universities, supported by targeted grants and curriculum modernisation initiatives (Ministry of Science and Higher Education). As of early 2026, approximately 15,000 students are enrolled in AI-specialised degree programmes nationwide, marking a multi-fold increase compared with the mid-2010s.

Flagship institutions such as Moscow State University (MSU), ITMO University, Moscow Institute of Physics and Technology (MIPT), Skoltech, and the Higher School of Economics (HSE) have established dedicated AI faculties or interdisciplinary tracks, frequently co-designed with industry partners. ITMO’s AI360 programme, developed with Yandex and Sber, exemplifies the applied model combining coursework, internships, and project-based learning (ITMO AI360).

In 2025, the Government’s Analytical Center launched an Educational Program Expertise Center, selecting 22 universities to train advanced AI specialists and 26 universities to strengthen broader IT capacity, with the explicit goal of producing “world-class specialists” aligned with national priorities (Government Analytical Centre, 2025). Faculty capacity-building has accelerated in parallel: more than 5,000 lecturers and instructors were retrained in AI-related disciplines during 2024–2025 through federally supported programmes.

The National AI Strategy sets an explicit quantitative ambition to increase annual AI graduates from roughly 3,000 in 2022 to more than 15,000 by 2030, a target reaffirmed in the 2023–2024 strategy updates (Government of Russia, AI Strategy update). Russian universities are also encouraged to internationalise curricula and research outputs to maintain global competitiveness, and Russian institutions continue to perform strongly in mathematics, programming, and computer science rankings, despite reduced integration with Western academic networks (QS Subject Rankings).

Workforce Upskilling and Retention

Beyond pre-career education, Russia has expanded large-scale workforce upskilling programmes to support AI diffusion across the economy. By 2023, the number of IT specialists in Russia exceeded 1.08 million, a roughly 50 percent increase over five years, driven in part by state-backed retraining initiatives (MinTsifry labour statistics). Programmes such as TOP-AI, launched by the Ministry of Digital Development, target students and mid-career professionals with advanced AI training, aiming to prepare 10,000 specialists by 2030 in areas such as deep learning, computer vision, and data engineering (MinTsifry TOP-AI).

Online platforms and corporate universities play a major role. SberUniversity and Yandex’s School of Data Analysis continue to train thousands of engineers annually, while subsidised online courses allow workers in traditional sectors to transition into AI-related roles (SberUniversity; Yandex SDA). Public-sector upskilling is also prioritised, with training programmes for civil servants, teachers, and healthcare professionals to support AI-enabled service delivery, including diagnostic AI in medicine (Ministry of Health, Russia).

The 2023 update to the National AI Strategy explicitly identifies skills shortages as a structural constraint and links workforce policy to national resilience goals (Regulations.ai summary). Incentives such as funded doctoral places, competitive salaries in state research centres, and targeted grants for young scientists are intended to retain talent domestically. Official projections suggest the AI workforce could expand several-fold by the mid-2030s if education and retention measures succeed, though authorities acknowledge significant execution risk (Government Analytical Centre).

Brain Drain and Talent Mobility

Talent mobility remains one of the most acute challenges facing Russia’s AI ambitions. Since 2022, large-scale emigration of IT professionals has been widely reported, with independent estimates ranging from several hundred thousand to over a million departures, many involving highly skilled engineers (Carnegie Endowment; The Bell). This outflow has heightened the strategic importance of retention and repatriation measures.

Policy responses focus on creating domestic opportunity and prestige. The expansion of well-funded AI research centres, preferential treatment for IT and AI professionals, and high-visibility national projects are designed to anchor top talent within Russia (Ministry of Digital Development). Russia has also simplified visa and tax regimes for foreign specialists in priority technology areas, though geopolitical conditions limit inflows from Western countries (Government of Russia, migration policy). Engagement with the Russian-speaking tech diaspora continues through conferences such as AI Journey, hosted annually by Sber, and through collaborative research initiatives (AI Journey).

Internal mobility is also actively managed. Engineers are incentivised to work on public-sector and defence-related AI projects through competitive compensation and career fast-tracking, including roles within dedicated military and security AI units (TASS, defence AI coverage). At the same time, the government frames ethical governance and social legitimacy as retention tools: the Code of Ethics for Artificial Intelligence, signed by hundreds of organisations, is presented as a means of ensuring AI development aligns with societal values and provides a stable professional environment (AI Ethics Code).

Overall, as of January 2026, Russia’s talent, education, and mobility strategy reflects a dual imperative: rapid domestic talent expansion and containment of brain drain. While education pipelines are scaling rapidly, the long-term success of the AI strategy depends on whether Russia can convert training capacity into sustained, high-quality human capital under prolonged external constraints.

Part 4: Regulatory Architecture, Deployment Pathways & Global Influence

4A. 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

4B. Industrial Deployment & Tech Diffusion

Russia’s approach to AI deployment prioritises strategic industries where scale, state demand, and data availability intersect. Rather than diffuse, market-led adoption, AI diffusion is driven through healthcare, transport and urban systems, and energy-industrial complexes where public procurement, regulatory sandboxes, and state-aligned firms can accelerate real-world deployment. By January 2026, these sectors collectively represent the most mature pathways through which AI has moved from pilot projects into operational systems, providing a practical testbed for Russia’s broader ambitions in digital sovereignty and applied AI.

Healthcare & Social Services

Healthcare is Russia’s most advanced civilian AI deployment sector. Moscow has operated one of the world’s largest city-level AI programmes in radiology since 2020, using a formal experimental regime to integrate computer vision into public hospitals and clinics. City authorities report that millions of diagnostic studies have been processed using AI decision-support tools, and that the municipal AI portfolio now spans around 100 active projects across healthcare, transport, utilities, and urban services (Moscow Government, Mar 2023; Moscow Government, Dec 2023; ai.mos.ru).

Expansion continued through 2024–2025, including the publication of open annotated datasets for head CT to support developer training and model benchmarking, and mayoral announcements confirming sustained scaling of medical AI tools across the city’s healthcare system (Moscow Open Data, Feb 2024; Mayor of Moscow, Feb 2025; Moscow News, Jun 2025).

At the federal level, the Ministry of Health operates the National AI in Healthcare Platform, designed to connect clinicians and developers, publish clinical tasks and datasets, and provide controlled access to training data for accredited firms (ai.minzdrav.gov.ru; Ministry of Health, Nov 2022). Ministry updates through 2024–2025 highlight the nationwide rollout of AI decision-support tools, including speech-to-text systems for clinical documentation and certified AI medical products registered as clinical decision aids (Ministry of Health, Feb 2024; Mar 2024; Dec 2024; Regional adoption, Jan 2025). Healthcare thus represents Russia’s clearest example of AI transitioning from experimentation to institutionalised service delivery.

Mobility, Logistics & Urban Management

Transport and urban systems form the second major deployment pillar. Russia operates nationwide experimental legal regimes (ELRs) for highly automated road transport, established by government resolution in December 2020 and extended through subsequent acts. These regimes permit testing of driverless vehicles on public roads, including remote dispatch and operation without a safety driver in defined zones (Government Resolution, Dec 2020; ELR conditions, Mar 2022; Government News, Mar 2022).

In freight transport, the government has highlighted driverless KAMAZ truck pilots on the M-11 “Neva” corridor between Moscow and St. Petersburg as part of broader logistics automation efforts (Government of Russia, Jun 2023). Parallel sandboxes support unmanned aviation systems and urban service automation under controlled regulatory conditions (Government Act, May 2024).

Moscow continues to act as a national urban AI testbed. In 2024–2025, the city launched platforms enabling full-cycle AI development, including access to datasets, source code repositories, and challenge tasks for local developers (Moscow Government, May 2024; Mos.Hub updates, Feb 2025; Apr 2025). These initiatives position transport, logistics, and city management as scalable proving grounds for AI diffusion beyond pilot cities.

Energy, Extractives & Manufacturing

Heavy industry and energy represent Russia’s most strategically significant AI deployment domain, driven by large state-aligned firms. Gazprom Neft reports extensive use of AI in oil and gas operations, including chemical formulation (“digital molecules”), drilling optimisation, and infrastructure monitoring, and has promoted AI-based construction supervision standards at the national level (Gazprom Neft, Oct 2024; Digital oilfield overview, 2022; Infrastructure monitoring, 2021; AI construction standard, Apr 2025). These deployments illustrate AI’s role in efficiency, safety, and cost control across extractive industries.

In the nuclear and utilities complex, Rosatom has positioned AI as a competitive advantage, applying it to predictive maintenance, digital twins, and smart heat and city infrastructure. Company disclosures and interviews cite dozens to over a hundred AI projects spanning industrial operations and municipal services (Rosatom interview, Jan 2024; Rosatom smart heat networks, 2023).

To sustain these deployments, industrial firms are investing in dedicated workforce pipelines. In 2025, Gazprom Neft and ITMO University announced expanded joint master’s programmes in AI and data science aligned with national projects, explicitly linking higher education to industrial AI needs (Gazprom Neft–ITMO partnership, Jun 2025; Jul 2025).

Across these sectors, AI diffusion in Russia is sector-led rather than economy-wide, with healthcare, transport, and energy functioning as anchor domains where regulatory flexibility, state demand, and corporate scale converge. By January 2026, these industries form the practical backbone of Russia’s applied AI landscape and the primary channels through which national AI capabilities are operationalised.

4C. International Engagement & Standards

Russia’s international engagement on artificial intelligence is shaped by a preference for continuity, sovereignty, and standards-based influence rather than the creation of new binding regimes. By January 2026, Moscow positions AI within existing multilateral arms-control, economic, and technical standard-setting frameworks, while advancing its interests through selective leadership in standards bodies and coordination with non-Western partners. This approach reflects an effort to preserve strategic flexibility, avoid restrictive norms that could constrain domestic development, and embed Russian technical and regulatory preferences into international practice through multilateral and regional forums.

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

Part 5. 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.

5A. Performance Metrics & Monitoring

Russia has formalised a relatively comprehensive system of performance metrics and monitoring mechanisms to track progress under its National AI Strategy and to adapt policy in response to measured outcomes. The framework is anchored in presidential decrees and government roadmaps that define key performance indicators (KPIs) across economic impact, research output, human capital, technology adoption, and public trust. A major inflection point came with the 2023–2024 update to the National AI Strategy, which explicitly introduced expanded KPIs and acknowledged implementation gaps, including computing constraints, skills shortages, and uneven adoption across government (Presidential Decree No. 490, 2019; Government of Russia, AI Strategy amendments; TASS, 16 Feb 2024). Federal ministries are required to align sectoral plans with these indicators and report progress, while regions and state-owned enterprises are instructed or encouraged to adopt compatible metrics, reinforcing a whole-of-government monitoring model.

The strategy retains time-bound reference points for 2024 and 2030. While the 2024 milestone was framed as a point of “significant improvement,” official assessments published during the period indicated uneven progress, prompting recalibration rather than abandonment of targets. The 2030 horizon remains the principal benchmark, with updated metrics designed to track not only ambition but also feasibility under sanctions and long-term technological constraints (Regulations.ai summary).

Economic and Industry Impact Metrics

A central performance objective is AI’s contribution to economic growth and productivity. Official strategy documents and ministerial statements project that AI-enabled technologies could contribute over ₽11 trillion to GDP by 2030, equivalent to roughly 6–10 percent of GDP, up from negligible levels in the early 2020s (Government Analytical Centre; Reuters, 19 Nov 2025). To operationalise this ambition, policymakers track indicators such as the size of the domestic AI market, AI-related investment volumes, and adoption across priority industries.

Investment metrics function as leading indicators. Government projections target annual AI-related investment approaching ₽800–900 billion by the end of the decade, combining public funding with mandated or incentivised private investment from state-aligned firms (AEB Digital Law Digest, 2024). Structural indicators include growth in the number of organisations developing or deploying AI, with a target to increase the population of AI-active firms by around 50 percent relative to early-2020s baselines. These indicators are monitored through national programmes, procurement data, and corporate disclosures.

Performance monitoring extends to regional and sectoral deployment. Regions are encouraged to embed AI targets into their digital economy plans, and ministries track adoption in priority sectors such as healthcare, transport, energy, and public administration. In healthcare, federal guidance sets expectations for the deployment of certified AI solutions in each region by 2030, providing a concrete benchmark for diffusion (Ministry of Health, Russia). These indicators allow policymakers to assess whether AI-driven growth is geographically and sectorally broad-based rather than concentrated in a few urban or corporate hubs.

Research and Innovation Outputs

Innovation performance is monitored through bibliometric, patent, and technology-output indicators. The strategy explicitly calls for growth in the number and international impact of Russian AI publications, patents, and technical solutions, with publication quality and citation metrics increasingly used to assess research effectiveness (Government of Russia, AI Strategy update). Ministries track patent filings, open-source contributions, and the creation of reusable AI tools and libraries as indicators of innovation depth and spillover potential.

Baseline comparisons remain central. International benchmarks consistently show Russia trailing the United States, China, and leading European economies in frontier AI research capacity, high-end computing, and startup formation. Russia’s limited presence in global supercomputing rankings and AI startup counts reinforces the emphasis on targeted research centres and flagship programmes (TOP500; OECD AI Policy Observatory). Government-linked analytical bodies publish periodic assessments comparing Russia’s innovation indicators with global peers, and these comparisons are explicitly used to justify reallocating funding toward underperforming or strategically critical areas (Government Analytical Centre).

Recognition in international competitions, selective research collaborations, and the adoption of Russian-developed AI systems in domestic industry are also treated as qualitative indicators of innovation success. These outputs provide feedback on whether public R&D investment is translating into deployable technologies rather than remaining confined to academic settings.

Talent Development Indicators

Human capital is treated as a measurable strategic asset. The National AI Strategy sets explicit targets for expanding the AI-skilled workforce and mainstreaming AI literacy, including an ambition for up to 80 percent of the workforce to possess basic AI-related skills by 2030, compared with single-digit percentages in the early 2020s (Government of Russia, AI Strategy). Progress toward this objective is tracked through enrolments in AI degree programmes, participation in retraining initiatives, and completion of certified AI courses.

Education ministries report on the number of AI-specialised graduates, with targets to increase annual output from around 3,000 in 2022 to over 15,000 by 2030 (Ministry of Science and Higher Education). Additional indicators include the number of universities offering AI programmes, the retraining of teaching staff, and the international standing of Russian institutions in computer science and related fields (QS Subject Rankings).

Retention and mobility are monitored more qualitatively. Policymakers track trends in emigration of IT specialists, return migration of researchers, and staffing levels at national AI research centres. While precise figures are politically sensitive, official statements increasingly acknowledge talent outflows as a risk factor, reinforcing the link between talent metrics and broader resilience objectives (Carnegie Endowment).

Adoption and Public Sentiment Metrics

A distinctive feature of Russia’s monitoring framework is the inclusion of public trust and societal acceptance as explicit performance indicators. Strategy revisions identify trust in AI as a condition for sustainable deployment, with authorities signalling the use of surveys and public-opinion monitoring to track attitudes toward AI in healthcare, public services, and security applications (Regulations.ai). This reflects concern that resistance to AI could undermine adoption even where technical capacity exists.

Adoption metrics complement sentiment tracking. Federal and regional bodies are required to document AI deployments in public services, and ministries report on the number and scope of AI use-cases implemented. Enterprise adoption is assessed through disclosures by large state-owned companies and regulated industries, including indicators such as the proportion of business processes augmented by AI or AI-related capital expenditure (Ministry of Digital Development). Low adoption in government was explicitly identified as a weakness in earlier assessments, prompting mandates for pilot projects and structured reporting.

Overall, Russia’s performance monitoring framework reflects a data-driven but adaptive approach. Metrics are used not only to signal ambition but to identify constraints, recalibrate targets, and justify policy adjustments. By extending monitoring beyond GDP and research outputs to include adoption and public trust, the strategy seeks to ensure that AI progress is measured in terms of real economic, institutional, and social impact rather than headline technological claims alone.

5B. Strategic Foresight & Resilience

Russia’s national AI strategy is explicitly framed around strategic foresight and systemic resilience, reflecting an assumption that technological development will unfold under prolonged geopolitical pressure, economic volatility, and adversarial competition. Policy documents and senior-level statements consistently stress the need to anticipate disruption, identify structural bottlenecks early, and design AI development pathways that remain viable under sanctions, supply shocks, and regulatory fragmentation. This logic has become more pronounced since 2022, with AI increasingly treated as a long-cycle strategic capability rather than a purely commercial technology (Presidential Decree No. 490; Government of Russia, AI Strategy amendments).

Strategic foresight is institutionalised through periodic strategy updates, scenario analysis conducted by analytical bodies under the Government and the Security Council, and tighter feedback loops between performance monitoring and policy adjustment. The 2023–2024 amendments to the National AI Strategy explicitly recognised new constraints, including limited access to advanced hardware, talent shortages, and uneven adoption across government, and recalibrated objectives accordingly (TASS, 16 Feb 2024; Regulations.ai summary). This adaptive posture underpins Russia’s broader resilience narrative: rather than assuming uninterrupted access to global markets and technologies, the strategy assumes disruption as a baseline condition.

Confronting Sanctions Constraints

Western sanctions and export controls on advanced technologies represent the most immediate stress test of Russia’s AI resilience. Since 2022, restrictions on high-performance semiconductors and manufacturing equipment have sharply limited access to cutting-edge GPUs and accelerators essential for training large AI models (U.S. BIS export controls overview). Russian officials have publicly acknowledged that these measures constrain near-term AI ambitions, particularly in frontier model development (Reuters, 19 Nov 2025).

Industry leaders echo this assessment. Sber’s CEO has described access to high-end GPUs as one of the most difficult bottlenecks for domestic AI projects, reinforcing the government’s emphasis on monitoring and expanding national computing capacity (Reuters, 19 Nov 2025). In response, Russia has adopted a set of short- and medium-term resilience measures, including prioritising allocation of existing hardware, optimising software efficiency to reduce compute intensity, and tracking supercomputing capacity as a formal performance indicator within the AI strategy (Government of Russia, AI Strategy amendments).

Foresight planning explicitly assumes that sanctions will persist. Policy documents and expert commentary suggest a shift away from dependence on state-of-the-art hardware toward stability-oriented development, using slightly older nodes and available architectures while building domestic alternatives. This approach prioritises continuity of AI progress over frontier leadership, reflecting a deliberate trade-off embedded in the resilience logic (OECD AI Policy Observatory – Russia profile).

Technological Sovereignty and Import Substitution

Technological sovereignty functions as the central organising principle of Russia’s AI resilience strategy. Import substitution programmes, already present before 2022, have been expanded and explicitly linked to AI development across hardware, software, and platforms (Ministry of Industry and Trade). State-backed entities such as Rostec, Skolkovo, and sectoral development funds support domestic chip design, AI accelerators, and specialised processors, while established firms such as MCST and Baikal Electronics are encouraged to adapt CPUs for AI workloads. Although these efforts remain several generations behind global leaders, policy documents emphasise incremental progress and reduced dependency rather than parity (Government Analytical Centre).

Parallel efforts focus on the software and platform layer. Authorities promote domestic AI frameworks, open-source libraries, and Russian cloud platforms as alternatives to Western APIs and hyperscalers. Preferential procurement rules and import-substitution quotas in critical infrastructure require state agencies and state-owned enterprises to prioritise domestic software solutions where available, embedding sovereignty objectives into purchasing and deployment decisions (Ministry of Digital Development).

Where full substitution is not immediately feasible, Russia has sought to diversify external dependencies. Cooperation with non-Western partners, particularly China, plays a bridging role in hardware supply, telecommunications infrastructure, and selected AI R&D projects. Russian officials have openly acknowledged reliance on Chinese technology as an interim measure while domestic capabilities mature (Reuters, 19 Nov 2025). This combination of domestic investment, selective substitution, and alternative partnerships forms the core of Russia’s technological resilience posture.

Centralised Coordination and Civil–Military Integration

Resilience is also pursued through centralised coordination and tighter integration across civilian and defence AI efforts. The government has strengthened top-down oversight to reduce fragmentation and accelerate decision-making, directing ministries and state-owned enterprises to align AI initiatives with national priorities and shared performance indicators (Government of Russia). This approach reflects lessons drawn from early implementation gaps, where dispersed initiatives failed to achieve scale or coherence.

The war in Ukraine has further accelerated civil–military convergence in AI development. Defence requirements now feed directly into national AI planning, while civilian firms and universities increasingly participate in dual-use projects related to autonomous systems, surveillance, logistics, and decision-support tools (TASS, defence technology coverage). This integration is framed as a resilience measure: pooling talent, data, and infrastructure across domains reduces duplication and creates redundancy if one sector faces funding or supply disruptions.

From a foresight perspective, this model also anticipates the future character of conflict and competition, where AI-enabled systems blur the boundary between civilian and military use. Central coordination allows the state to reallocate resources rapidly and prioritise capabilities deemed critical under changing threat scenarios.

Alliances and International Collaboration

International engagement functions as an external resilience layer. Facing reduced access to Western AI ecosystems, Russia has sought to embed its AI development within non-Western multilateral and regional frameworks, particularly BRICS and BRICS+ formats. In late 2024, Russian leadership announced the formation of an AI Alliance Network among BRICS members and partner countries, intended to support joint research, talent exchange, and coordinated development of AI technologies and standards (Government of Russia, Dec 2024).

This alliance-building serves multiple resilience objectives: access to alternative markets, exposure to diverse datasets and use cases, and collective influence in international standard-setting. Russian officials argue that cooperation with countries such as China, India, Brazil, and South Africa can offset isolation from Western markets and reduce vulnerability to unilateral pressure (Ministry of Foreign Affairs). Analysts note that deeper AI cooperation with China, in particular, could partially mitigate Russia’s hardware and platform constraints, even if it does not eliminate dependency risks.

Russia also engages in global forums to shape AI governance norms in ways consistent with its resilience objectives. At the United Nations and related bodies, it has opposed blanket bans on military AI systems, instead advocating incremental regulation and expert-led processes, while simultaneously endorsing the development of universal ethical principles that emphasise sovereignty and non-discrimination (UN CCW statements). This dual approach reflects strategic foresight: participating in rule-making reduces the risk that international norms will be set in ways that constrain Russia’s technological options.

Overall, Russia’s strategic foresight and resilience framework for AI reflects a realist assessment of long-term constraints. By assuming sustained sanctions, contested norms, and fragmented technology markets, the strategy prioritises continuity, control, and adaptability over rapid convergence with global leaders. Whether this approach can sustain innovation momentum remains uncertain, but it has clearly reshaped Russia’s AI ambitions around endurance rather than speed.

5C. Public Trust, Inclusion & Social Equity

Russia’s AI strategy treats public trust and social legitimacy as practical enablers of deployment, not merely normative aspirations. Strategy amendments explicitly introduce trust in AI technologies as a measurable national objective, while policy instruments combine “soft” ethics frameworks, sectoral guidance (notably in healthcare), and state-led diffusion programmes intended to ensure that AI adoption extends beyond elite urban and corporate ecosystems. At the same time, Russia’s approach operates in a high-surveillance governance environment, creating an inherent tension: some of the fastest-scaling AI use cases (especially computer vision) can improve public services and security, yet also raise significant concerns about privacy, oversight, and misuse, which can undercut trust if not credibly governed (TASS on trust KPI in the updated strategy; Human Rights Watch on facial recognition risks).

Ethical Principles & Governance

Russia’s primary national ethics instrument is the Code of Ethics in the Field of Artificial Intelligence, adopted in October 2021 as a voluntary framework applying to civil (non-military) AI systems. The Code sets high-level principles including human rights considerations, transparency in human–AI interaction, privacy/data protection, safety, and accountability (AI Alliance – Code PDF; Regulations.ai summary). This “soft regulation” approach has been framed domestically as a way to build an “environment of trusted development” without freezing innovation through overly prescriptive law (AI Alliance ethics page; UNESCO Russia note on “soft” regulation).

Uptake has expanded materially. Reporting in 2024 indicates 423 organisations had signed the national AI ethics code, signalling broad institutional buy-in across business, academia, and public entities (DataGuidance, Sep 2024). This is complemented by a growing pattern of sectoral ethics guidance. In healthcare, multiple outlets reported that the Ministry of Health developed/approved a Code of Ethics for AI in healthcare in late 2024–2025, emphasising patient safety, confidentiality, and limits on AI autonomy in clinical settings (Oreanda, Dec 2024; GXP News, Dec 2024; AsiaMC digest, Mar 2025).

The updated National AI Strategy also elevates trusted AI and risk management as implementation expectations, indicating a gradual move from purely voluntary guidance toward more formalised governance in high-risk domains (TASS on updated decree and KPIs; Regulations.ai on updated strategy).

Public Awareness & Engagement

Public trust is pursued through a mix of mass communication, high-profile convenings, and stakeholder signalling. The AI Journey conference, hosted by Sber and regularly featuring presidential participation, is the most visible national forum for promoting AI success stories and signalling official priorities. In 2025, President Putin explicitly argued that reliance on foreign large-language models is unacceptable due to information influence risks and called for domestic generative AI products, data centres, and supporting energy infrastructure—framing sovereign AI as both a security measure and a national development project (Reuters, Nov 2025).

Russia also uses measurable targets to reinforce the trust agenda. The updated decree states that the citizens’ level of trust in AI technologies should rise to at least 80% by 2030, from 55% in 2022, effectively making trust a monitored KPI rather than an abstract aspiration (TASS, Feb 2024; TAdviser summary quoting the target).

Regional Inclusion & Digital Access

Regional inclusion is pursued through a combination of connectivity programmes and diffusion mandates in public services. Russia’s national Digital Economy programme has long articulated targets to expand broadband connectivity beyond major cities, reflecting the view that digital infrastructure is a prerequisite for equitable AI access. External reporting notes that the programme aimed to raise fixed broadband coverage significantly nationwide (including high-speed targets by 2024) (Freedom House, citing Digital Economy goals).

A major implementation channel has been Rostelecom’s “Bridging the Digital Divide” efforts, which describe connecting thousands of small communities and expanding connectivity to healthcare and other socially significant facilities (Rostelecom CSR overview). These efforts are essential for enabling AI-enabled services such as telemedicine triage, remote diagnostics, and data-driven public administration in remote regions.

In addition, the National AI Strategy’s implementation logic increasingly encourages regions to integrate AI into local development plans and to adopt standardised public-service AI solutions once certified. In practice, healthcare has been one of the main vehicles for “equity through diffusion,” with national programmes pushing clinical AI tooling beyond Moscow into regional health systems (for example through national platforms and approved AI medical products) (Ministry of Health; ai.minzdrav.gov.ru).

Equitable Benefits & Social Impact

Russia’s stated social-equity case for AI is strongest in sectors where AI can measurably reduce service gaps: healthcare access, education quality, infrastructure reliability, and disaster/environmental monitoring. The Moscow radiology programme provides a visible example of how AI is framed as service augmentation: Moscow reports 13 million radiology tests processed with AI support, positioning AI as a tool to expand diagnostic capacity and reduce clinician workload (mos.ru, Sep 2024). Nationally, similar narratives underpin the expansion of AI decision-support tools, including clinical documentation assistance and certified diagnostic systems, especially in regions with specialist shortages (Ministry of Health).

However, the trust–equity agenda must also be evaluated against the rapid expansion of surveillance-related AI. Public reporting cites very large deployments of facial recognition-linked camera systems; Eurasianet reports over 1 million surveillance cameras in Russia and that about one in three is connected to facial recognition, citing the Minister for Digital Development, with ~230,000 in Moscow (Eurasianet, Jun 2024). Human rights organisations argue that weak oversight and limited transparency in such deployments can undermine rights and public trust, even where authorities justify them as improving security or service delivery (Human Rights Watch, Sep 2021). This tension is central to Russia’s trust challenge: broad diffusion can increase access and capability, but highly visible coercive or opaque applications can erode legitimacy.

Overall, Russia’s approach to public trust, inclusion, and social equity is best characterised as state-orchestrated trust-building plus diffusion-by-programme: voluntary ethics frameworks and sectoral codes are used to signal responsibility, connectivity programmes and national platforms support regional access, and quantified trust targets provide a mechanism for monitoring social acceptance. The durability of trust will depend on whether governance mechanisms meaningfully constrain misuse in high-risk domains—especially surveillance—while maintaining the practical benefits that drive adoption.

Part 6: Evolution of Russia's National AI Strategy (2015–2025)

This section traces the evolution of Russia’s national artificial intelligence strategy from its early foundations in the pre-2015 period through to the present, identifying major policy milestones and strategic shifts over time. It examines the formative phase of state-led investment in digital government systems, defence and security research, computing infrastructure, and scientific institutions; the subsequent consolidation of AI as a national strategic priority from the mid-to-late 2010s; and the expansion into coordinated deployment, regulation, and international engagement. Across these phases, Russia’s AI trajectory reflects a sustained effort to align technological sovereignty, state capacity, and national security objectives with industrial modernisation and resilience under increasingly contested global technology conditions.

6A. 2024–2025. Sanctions-Era Execution, Sovereignty & Strategic Repositioning

Execution under constraint and strategic hardening
During 2024–2025, Russia’s national AI strategy shifted decisively from ambition-setting to execution under sustained external constraint. The February 2024 presidential amendments to the National AI Strategy explicitly acknowledged structural bottlenecks, including shortages of high-performance computing capacity, dependence on foreign hardware, and talent gaps, and reframed AI as a long-cycle strategic capability rather than a near-term growth engine (TASS, 16 Feb 2024; Government of Russia, updated strategy text). This marked a clear pivot toward resilience and continuity, with policy emphasis placed on making existing programmes deliver under sanctions rather than expanding scope or ambition.

Compute, generative AI, and energy–infrastructure coupling
A defining development in this period was the explicit linkage of AI, compute infrastructure, and energy policy. At the AI Journey conference in November 2025, President Putin called for the creation of a national AI task force and warned against reliance on foreign large language models, arguing that domestic foundation models, data centres, and power infrastructure must be developed as an integrated sovereign system (Reuters, 19 Nov 2025). This intervention underscored a shift in thinking: generative AI was no longer treated as a standalone software challenge, but as a systems-level issue encompassing compute availability, energy supply, and national security. The practical response focused on optimising model efficiency, expanding domestic data-centre capacity, and scaling Russian-developed models within existing hardware limits rather than pursuing frontier performance.

Governance tightening and measurable delivery
Governance reforms during 2024–2025 prioritised execution discipline over regulatory expansion. Updated KPIs were embedded more firmly into national project monitoring, requiring federal ministries and major state-owned enterprises to report on AI deployment, compute utilisation, and progress against trust and adoption targets (Regulations.ai, Russia strategy update). Rather than introducing comprehensive new AI legislation, the government focused on sector-specific rules and “trusted AI” requirements in high-risk areas such as healthcare and critical infrastructure, reflecting a preference for incremental oversight that would not slow deployment.

Industrial repositioning around champion firms
Industrial execution continued to be dominated by a small group of state-aligned champions. Firms such as Sber and Yandex consolidated their roles as national AI platforms, integrating domestic models into banking, cloud services, and public-sector applications, while energy and industrial conglomerates accelerated AI use for optimisation and resilience (Sber AI disclosures; Yandex AI platforms). The state’s role during this phase was less about stimulating new market entry and more about ensuring that these incumbents could sustain investment and deployment under constrained capital and technology conditions.

International posture and strategic repositioning
Externally, Russia’s AI posture during this period moved further away from Western-centric frameworks toward selective alignment with non-Western partners. AI cooperation was increasingly framed within BRICS and BRICS+ contexts, emphasising digital sovereignty, opposition to technology discrimination, and resistance to binding constraints on military or dual-use AI systems (Ministry of Foreign Affairs of Russia; Government of Russia, BRICS cooperation statements). Rather than seeking leadership in global AI norm-setting, Russia prioritised preserving strategic flexibility and embedding its positions within existing multilateral processes such as UN expert groups.

From acceleration to endurance
Overall, the 2024–2025 phase marked a transition from acceleration to endurance. Russia’s AI strategy became less focused on catching up with frontier leaders and more oriented toward sustaining a functional, sovereign AI ecosystem capable of supporting national priorities over time. Execution, resilience, and control replaced scale and openness as the dominant themes, redefining success in terms of continuity and strategic autonomy rather than global market share or headline innovation metrics.

6B. 2020–2023. State-Led Scaling, Champion Firms & Governance Consolidation

This period marks the consolidation of Russia’s national AI strategy from early mobilisation into state-led scaling, characterised by the dominance of national champion firms and the gradual formalisation of governance structures. Between 2020 and 2023, AI moved from pilot initiatives toward coordinated deployment across priority sectors, while the state strengthened its role as strategic orchestrator, regulator, and principal customer. Viewed in reverse chronological order, developments in this period reveal an increasing emphasis on sovereignty, execution discipline, and institutional control.

2023. Industrial Deployment and Technological Sovereignty

By 2023, Russia’s AI strategy had narrowed its focus toward economic and industrial application under conditions of mounting external constraint. Strategic documents and official communications emphasised AI as a tool for automation, productivity, and resilience in sectors such as energy, agriculture, manufacturing, logistics, and transport. This orientation was codified in the 2023 update to the National AI Strategy, which formally acknowledged limitations in computing capacity, access to advanced semiconductors, and availability of skilled personnel, and repositioned AI as a stabilising capability essential to technological sovereignty rather than a purely growth-driven technology (Government of the Russian Federation, National AI Strategy update, 2023: http://government.ru/docs/48455/).

The updated strategy reinforced preferences for domestic solutions in public procurement, infrastructure, and cloud services, and clarified the coordinating role of the federal government across ministries and state-owned enterprises. AI was increasingly framed as an instrument for reducing dependence on foreign platforms and sustaining industrial capacity under long-term sanctions pressure.

2022. National Strategy Assessment and Sectoral Expansion

In 2022, the Russian government published a comprehensive Progress Report on the implementation of the National AI Strategy adopted in 2019, representing the first systematic assessment of national AI performance (Government Analytical Centre, National AI Strategy Progress Report, 2022: https://ac.gov.ru/uploads/2-Publications/ai_report_2022.pdf). The report documented measurable advances in AI research, public–private cooperation, and sectoral adoption, particularly in smart cities, healthcare diagnostics, industrial automation, and defence-related applications.

At the same time, the assessment identified emerging weaknesses, including uneven regional uptake, persistent skills shortages, and increasing reliance on foreign hardware and software components. These findings played a critical role in shaping subsequent policy recalibration, providing the empirical basis for the sovereignty- and resilience-oriented adjustments introduced in later strategy updates.

2021. Ethical Governance and Institutional Legitimacy

Governance consolidation advanced in 2021 through the introduction of Russia’s first national Code of Ethics for Artificial Intelligence, a voluntary framework applicable to civilian AI systems (Alliance in the Sphere of Artificial Intelligence, Code of Ethics for AI, 2021: https://a-ai.ru/wp-content/uploads/2021/10/Code-of-Ethics.pdf). The code articulated principles including human oversight, transparency in AI–human interaction, data protection, non-discrimination, and accountability, and was endorsed by leading technology firms and public institutions.

Rather than pursuing immediate statutory regulation, authorities adopted a soft-law approach intended to embed ethical norms within industry practice while preserving flexibility for innovation. The ethics framework was incorporated into the federal AI project and aligned with the Strategy for the Development of the Information Society (2017–2030), signalling that trust, legitimacy, and responsible deployment were becoming formal policy objectives alongside scale and performance (Regulations.ai, Russia AI Ethics Code summary: https://regulations.ai/regulations/russia-2021-10-ai-ethics-code).

2020. Champion Firms and Public–Private Coordination

The institutional foundation for state-led AI scaling was established in 2020 with the formation of the Alliance in the Sphere of Artificial Intelligence. Created with government support, the alliance brought together major national champions including Sber, Yandex, VK (formerly Mail.ru Group), MTS, Gazprom Neft, and the Russian Direct Investment Fund, creating a structured mechanism for coordination between industry, research institutions, and the state (Alliance in the Sphere of Artificial Intelligence, official site: https://a-ai.ru/?lang=en; TASS, coverage of alliance formation, 2020: https://tass.com/economy/1087734).

This alliance-centric model reflected Russia’s preference for champion-driven innovation over a startup-led ecosystem. Early priorities focused on natural language processing, computer vision, facial recognition, robotics, and AI deployment in critical infrastructure, particularly energy and transport. Through shared working groups, data initiatives, and coordinated policy feedback, the alliance became a central execution vehicle for the National AI Strategy.

6C. 2015–2019. National Strategy Formation & Capability Mobilisation

The period from 2015 to 2019 represents the formative phase of Russia’s national AI trajectory, during which artificial intelligence shifted from a set of fragmented research and defence initiatives into a codified national priority. This phase is characterised by early military-driven investment, expanding state-led R&D capacity, initial civilian deployment in urban governance, and, ultimately, the formalisation of AI as a strategic pillar of national development and security through presidential decree.

2019. National Strategy Formalisation and Presidential Mandate

In October 2019, Russia formally consolidated its AI ambitions with the adoption of the National Strategy for the Development of Artificial Intelligence to 2030, approved by Presidential Decree No. 490 and signed by President Vladimir Putin. The strategy articulated the objective of positioning Russia among the world’s leading AI powers by 2030 and framed AI as essential to economic competitiveness, national security, and technological sovereignty. It set out priorities across AI infrastructure, education and workforce development, data access, and deployment in strategic sectors including defence, healthcare, transport, and public administration (Kremlin, National AI Strategy, 2019: http://kremlin.ru/acts/bank/44528).

The 2019 strategy marked a turning point in governance. AI was no longer treated as a collection of sectoral initiatives but as a cross-cutting national capability requiring coordinated action across ministries, state-owned enterprises, research institutions, and leading technology firms. The document explicitly linked AI development to self-reliance and reduced dependence on foreign technology, foreshadowing the sovereignty-oriented framing that would intensify in later years.

2018. Defence-Centric AI and National Security Emphasis

Prior to formal strategy adoption, AI was already deeply embedded in Russia’s defence and national security planning. In 2018, the Ministry of Defence publicly highlighted AI as a transformative factor in future warfare, with investments directed toward autonomous weapons systems, military drones, decision-support tools, and battlefield analytics. Senior defence officials described AI as a means to enhance command-and-control, intelligence processing, and precision strike capabilities, reflecting a view of AI as a strategic military asset (Russian Ministry of Defence statements, 2018, summarised in TASS defence coverage: https://tass.com/defense).

This defence-led emphasis strongly influenced the shape of the later national strategy. AI development was framed not only in economic terms but as a component of deterrence, military modernisation, and strategic parity with peer competitors. The early prioritisation of defence applications also reinforced the state-centric and security-conscious orientation of Russia’s AI ecosystem.

2017. Expansion of State-Led AI Research and Industrial Champions

In 2017, Russia significantly expanded state-funded AI research and development, leveraging major public institutions and large, state-aligned firms. Investments flowed through research institutes under the Russian Academy of Sciences, defence-linked laboratories, and leading universities, while corporations such as Rostec, Sberbank, and Yandex emerged as central actors in applied AI development. Key areas included natural language processing, facial recognition, computer vision, and early autonomous systems (OECD AI Policy Observatory, Russia profile: https://oecd.ai/en/dashboards/countries/Russian-Federation).

This period saw the early formation of the champion-firm model that would later define Russia’s AI governance. Rather than relying on a broad startup ecosystem, the state increasingly depended on large firms with capital, data access, and political alignment to scale AI capabilities. Collaboration between public research institutions and these firms laid the groundwork for subsequent public–private coordination mechanisms.

2016. Smart Cities and Public Administration Pilots

Civilian deployment of AI began to accelerate in 2016 through smart city initiatives and public administration pilots, particularly in Moscow and Saint Petersburg. Municipal authorities launched early projects applying AI to traffic management, energy efficiency, video surveillance, and public service automation. These pilots demonstrated the operational value of AI in urban governance and generated large datasets that would later underpin more advanced AI systems (Moscow Government smart city programme overview: https://www.mos.ru/city/projects/smartcity/).

These early deployments also established cities as experimental environments for AI, allowing authorities to test regulatory approaches, procurement models, and public acceptance. The success of these initiatives reinforced confidence in AI as a practical governance tool and contributed to its elevation in national policy discussions.

2015. Early Military Investment and Capability Signalling

The roots of Russia’s AI mobilisation can be traced to 2015, when the Ministry of Defence began systematically investing in AI-enabled military technologies, particularly unmanned aerial vehicles, autonomous platforms, cyber defence, and algorithmic decision-support systems. This investment coincided with broader military modernisation efforts and reflected an early recognition that AI would shape future conflict dynamics (RAND analysis of Russian military modernisation, including AI: https://www.rand.org/pubs/research_reports/RR3099.html).

Although these initiatives were largely opaque, they played a critical signalling role within the state apparatus. AI was increasingly viewed as a strategic technology requiring long-term investment and coordination, setting the intellectual and institutional foundations for later civilian and economic applications.

6D. Pre-2015. Scientific Foundations, Digital State Building & Early AI Research

Russia’s pre-2015 AI foundations were shaped less by a standalone “AI strategy” than by three interlocking trajectories: a deep scientific tradition in mathematics and cybernetics, the steady build-out of digital-state infrastructure and data systems, and a modernisation agenda that began to identify frontier technologies as instruments of national competitiveness. These strands created the institutional, data, and industrial prerequisites that later enabled Russia to formalise AI as a national priority under the 2019 strategy.

A central enabling layer was digital-state capacity. Before the “Digital Economy” programme was approved in 2017, Russia’s digital transformation was organised through the state programme “Information Society (2011–2020)”, which established federal priorities around e-government services, information infrastructure, and ICT diffusion (Government of the Russian Federation, programme reference: https://government.ru/en/docs/3369/). In 2014, this direction was reinforced through the approval of the state programme by Government act No. 313 (15 April 2014), which is repeatedly cited as the formal basis for federal information systems and coordination of informatization (CIS Legislation reference to the 2014 approval: https://cis-legislation.com/document.fwx?rgn=87919). While not “AI policy” in itself, this architecture expanded digitised records, interoperability efforts, and service platforms that later became essential inputs for AI deployment in healthcare, urban management, and public administration.

A second foundation was the build-out of innovation infrastructure and applied R&D ecosystems. The Skolkovo Innovation Center, established by federal law in 2010, was designed as a legal-economic enclave to accelerate high-tech development in areas the state considered strategically important. It provided institutional scaffolding for R&D, startups, and university–industry collaboration that later proved directly relevant to AI-related fields (WIPO Lex, Federal Law No. 244-FZ on Skolkovo: https://www.wipo.int/wipolex/en/legislation/details/17100; Kremlin note on the law: https://en.kremlin.ru/acts/news/9056). This model normalised the use of special regulatory and fiscal regimes to stimulate priority technologies—an approach that would later recur in AI sandboxes and experimental legal regimes.

The third pre-2015 inflection was the emergence of a future-industry framing for advanced digital technologies. In his December 2014 address to the Federal Assembly, President Putin announced the launch of the National Technology Initiative (NTI) as a long-horizon programme to create Russian leadership in new high-tech markets, explicitly positioning it as a strategic industrial effort rather than a narrow research agenda (Kremlin transcript: https://en.kremlin.ru/events/president/news/47173). While NTI is broader than AI, its market roadmaps and competence-centre logic created organisational pathways through which AI-adjacent priorities—such as autonomous systems, neurotechnology, and digital platforms—could be funded, coordinated, and scaled. Later analyses of Russia’s digital sovereignty trajectory consistently treat the NTI launch as a key precursor to the state’s more explicit sovereign-tech posture in the late 2010s and beyond (Atlantic Council, 2024: https://www.atlanticcouncil.org/in-depth-research-reports/issue-brief/russias-digital-tech-isolationism/).

2014. Advanced Digital Technologies in the Modernisation Agenda

The most accurate way to describe the 2014 turning point is not that AI became “central” to a fully formed “Digital Economy Initiative” (the flagship Digital Economy programme was approved later, in 2017), but that advanced digital technologies—including AI-relevant capabilities—were increasingly embedded in national modernisation and future-industry planning. The combination of the Information Society programme’s digital-state build-out (https://government.ru/en/docs/3369/) and the launch of the National Technology Initiative (https://en.kremlin.ru/events/president/news/47173) created the two core conditions AI typically requires at national scale: (1) digitised systems that generate and store data, and (2) an institutional vehicle for prioritising and funding frontier technology markets.

Taken together, the pre-2015 period explains why Russia was able to move relatively quickly once AI was formally elevated later in the decade. Scientific capability provided the talent base; digital-state programmes created the datasets and administrative platforms; and innovation instruments such as Skolkovo and NTI supplied early coordination and investment channels. These foundations did not yet constitute a coherent national AI strategy, but they built the enabling environment on which Russia’s later state-led AI mobilisation would rely.