Kazakhstan is embarking on one of the most ambitious artificial intelligence projects undertaken by a mid-sized economy. Rather than treating AI as a purely private-sector or experimental technology trend, the country is framing it as a state-building project. Through the creation of a dedicated AI ministry, the passage of a comprehensive artificial intelligence law, the rollout of large-scale computing infrastructure, and the expansion of research and innovation institutions, Kazakhstan is positioning AI as a new national development platform.
In official discourse, AI is increasingly described in terms similar to how e-government was once framed during Kazakhstan’s earlier wave of public-sector modernization-but with far higher stakes. Unlike digital services reform, AI unfolds in a global environment defined by intense geopolitical competition, technological concentration, and unresolved questions around trust, ethics, and sovereignty. Kazakhstan’s leadership appears to view AI not only as a productivity tool, but as a strategic capability tied to economic diversification, state capacity, and regional influence.
The central question is whether this state-driven AI model can deliver. Will Kazakhstan succeed in building an AI-capable state and an export-oriented AI economy, or will its efforts remain fragmented-strong on announcements, but weaker on real productivity gains, institutional trust, and globally competitive firms? The answer will not be found in policy declarations alone. It will depend on how laws are enforced, how risks are managed, how talent pipelines are sustained, and whether public spending prioritizes measurable outcomes over symbolic projects.
Source: Euronews
From Strategy to Law: Kazakhstan’s Risk-Based AI Framework
A defining moment in Kazakhstan’s AI agenda came with the signing of Law No. 230-VIII “On Artificial Intelligence” on November 17, 2025. Entering into force on January 18, 2026, the law marks a decisive shift from strategic intent to binding regulation. In a regional context where many governments still rely on non-binding AI concepts or roadmaps, Kazakhstan has moved into enforceable governance territory.
The most important design feature of the law is its risk-based structure. Instead of regulating AI as a single, undifferentiated technology, the framework classifies systems according to levels of risk-often summarized as minimal, medium, and high risk. Regulatory obligations increase with the potential for harm, particularly in sensitive domains such as public administration, critical infrastructure, finance, healthcare, and security-related functions.
This approach mirrors emerging international best practices and allows flexibility: low-risk applications face lighter requirements, while high-risk systems are subject to stricter scrutiny, documentation, and oversight. In theory, this enables innovation without abandoning safeguards.
Equally significant are the law’s explicit prohibitions. It restricts manipulative AI practices, bans the exploitation of vulnerable populations, limits certain forms of social scoring, and places constraints on emotion recognition technologies unless explicit consent or narrowly defined exceptions apply. These provisions signal that Kazakhstan is aligning itself with a “human-centric” governance philosophy, even as it seeks to deploy AI extensively within the state.
Transparency occupies another central pillar of the law. AI-generated or synthetic content must be labeled, with both user-visible warnings and machine-readable markers. By addressing deepfakes and synthetic media directly, Kazakhstan treats them as governance and trust issues rather than peripheral technical curiosities.
Perhaps the most controversial-and consequential-element of the law concerns intellectual property. Kazakhstan has chosen to clarify that works generated entirely by AI, without meaningful human creative input, do not qualify for copyright protection. At the same time, the law allows for protection in cases where human involvement-such as prompt design or creative direction-meets certain thresholds. This reduces legal ambiguity for businesses and creators, but it also opens the door to future disputes over where that threshold lies.
The strength of the law lies in its clarity. Its weakness, or potential vulnerability, lies in implementation. Effective enforcement will depend on whether regulators, courts, and auditors have the technical expertise to classify systems correctly, assess compliance, and resolve disputes. Without that capacity, even the most sophisticated legal framework risks becoming procedural rather than substantive.
Enforcement, Procurement, and Trust: The Real Test of AI Governance
The true impact of Kazakhstan’s AI law will be determined by how it functions in practice. Three implementation challenges stand out.
First is regulatory capability. Risk-based governance only works if oversight bodies can genuinely assess AI systems-especially those classified as high risk. This requires skilled auditors, access to technical documentation, and independence from political or commercial pressure. If audits are superficial or symbolic, the system loses credibility.
Second is public-sector procurement. Many AI failures globally occur not because of bad algorithms, but because of flawed procurement processes: rushed pilots, unclear objectives, vendor lock-in, and weak post-deployment monitoring. Kazakhstan’s expanding state AI footprint makes procurement discipline essential. Clear performance metrics, sunset clauses, and continuous evaluation must become standard practice.
Third is public trust. Labeling AI-generated content is a step forward, but trust ultimately depends on whether citizens and businesses can understand, challenge, and appeal AI-influenced decisions. Without mechanisms for explanation and contestability, AI risks becoming a new form of opaque bureaucracy-efficient, perhaps, but socially brittle.
Kazakhstan faces a strategic choice. If the law becomes primarily an enabling tool for rapid state AI adoption, without visible accountability, public skepticism may grow. If, instead, it becomes a genuine trust-building architecture, Kazakhstan could distinguish itself as a governance leader in the region.
Source: Qazinform
Reorganizing the State Around AI
Kazakhstan has reinforced its legal push with institutional reform. In 2025, it established a dedicated Ministry of Artificial Intelligence and Digital Development, elevating AI from a sub-portfolio to a central pillar of national governance.
This move reflects an understanding that AI policy cuts across data governance, infrastructure, education, security, and industrial development. A specialized ministry can coordinate these domains more effectively than fragmented agencies. However, concentration of authority also raises governance questions. When one institution both promotes AI adoption and helps shape the rules governing it, transparency and oversight become critical.
The ministry operates alongside the government-approved Concept for AI Development for 2024-2029. While such concepts often risk becoming paper exercises, Kazakhstan has paired this framework with tangible actions: institutional creation, compute investment, and now binding regulation. The sequencing-concept, institutions, infrastructure, law, and scaling-is coherent and deliberate.
What remains missing is a single, transparent national scorecard for AI outcomes. Without clear metrics, it becomes difficult to distinguish genuine progress from activity. A credible scorecard would track audited public-sector deployments, productivity gains in priority sectors, adoption of domestic AI tools, access to compute for startups and universities, and spending on talent and data readiness relative to hardware.
Compute as Strategy: Supercomputers and Industrial Policy
One of Kazakhstan’s most visible AI investments has been large-scale compute infrastructure. The launch of a powerful supercomputing cluster, reportedly delivering up to two exaflops of FP8 performance using advanced GPUs, positions Kazakhstan among the most compute-capable states in Central Asia.
This matters because compute has become a strategic chokepoint. Access to affordable, scalable computing power underpins modern AI development-from training and fine-tuning models to deploying applications in industry, defense, and public services. For Kazakhstan, domestic compute capacity reduces reliance on external providers and supports data-sensitive workloads.
Yet compute strategies carry risks. Supercomputers can easily become prestige projects if access is restricted, governance is unclear, or commercialization pathways are weak. The strategic question is not the size of the cluster, but who can use it, under what conditions, and for what outcomes.
If compute access is transparent, competitively priced, and integrated with cloud tooling, research grants, and startup credits, it can energize the entire ecosystem. If access is narrow or politically mediated, the broader innovation landscape may stagnate.
Research Foundations and National-Language AI
Kazakhstan’s AI ambitions rest not only on infrastructure, but also on research capacity. The Institute of Smart Systems and Artificial Intelligence (ISSAI) at Nazarbayev University, established in 2019, serves as a critical anchor. Research institutions like ISSAI play roles that hubs and accelerators cannot: training advanced talent, publishing internationally, developing national-language resources, and providing independent technical credibility.
Kazakh-language AI is particularly strategic. Language models and speech systems are not merely cultural tools; they are inclusion mechanisms and sovereignty assets. Ensuring that AI systems function effectively in the national language helps prevent digital exclusion and reduces dependence on foreign models.
Kazakhstan has sought to expand beyond a single institute by launching new centers and signaling plans for AI-focused research universities. This diversification is healthy, but it also introduces coordination challenges. Overlapping mandates and diffuse accountability can dilute impact.
A clear division of labor-deep research institutions, applied labs, commercialization hubs, and a strong governance layer-would help preserve focus and efficiency.
Alem.ai and the Challenge of Ecosystem Building
The Alem.ai International Center for Artificial Intelligence represents Kazakhstan’s effort to create a visible, accessible “front door” to its AI ecosystem. Designed to host events, competitions, talent programs, and international collaboration, it is meant to make AI tangible and investable rather than abstract.
Such centers can either become showrooms or engines of growth. The difference lies in substance. For Alem.ai to function as an ecosystem flywheel, it must offer real value: access to compute credits, high-quality datasets with clear governance, procurement pathways into government and large firms, and mentorship with international credibility.
If it succeeds, Alem.ai can help democratize AI participation. If it remains event-driven, its impact will be limited.
Source: Algantic
Human Capital, Investment, and the Scale Challenge
Kazakhstan’s AI strategy includes an ambitious human-capital target: training up to one million people in AI-related skills by 2030. While headline figures should be treated cautiously, the emphasis on broad-based skills development is sound. AI adoption fails not because of hardware shortages, but because organizations lack people who can deploy systems responsibly and effectively.
The critical factor will be quality and linkage. Training must connect to real jobs, real deployments, and real organizational change. Otherwise, certifications risk becoming symbolic.
On the investment side, Kazakhstan has discussed large-scale venture initiatives, including a potential fund-of-funds model. The country already hosts the most active venture market in Central Asia, and AI increasingly cuts across sectors rather than standing alone.
The key is discipline. Funding should reward measurable outcomes, not the “AI” label. Government procurement, regulation, and standards can create market pull, turning the state into a demanding customer rather than a passive sponsor.
Strategic Risks on the Horizon
Three risks loom over Kazakhstan’s AI bet.
The first is legitimacy. If advanced AI deployments are concentrated in state and security contexts, public concerns around surveillance and fairness may intensify. Legal safeguards must be matched by visible enforcement.
The second is data governance and cybersecurity. AI thrives on data, but expanded access increases exposure. High-risk systems must be backed by strong security and independent evaluation.
The third is the temptation of prestige. Ministries, supercomputers, and centers generate headlines, but the real measure of success is productivity growth in sectors such as logistics, energy, healthcare, and education.
A Path Toward Durable Impact
Kazakhstan has already taken politically difficult steps: declaring AI a national priority, reorganizing the state, investing heavily, and passing a comprehensive law. The next phase is less glamorous but more decisive. Publishing an AI scorecard, separating promotion from oversight, treating compute as a public good, developing reference deployments, and codifying contestability for state AI systems would strengthen the entire project.
If Kazakhstan succeeds, it will do more than adopt AI. It will demonstrate how a resource-rich, strategically positioned state can translate digital modernization into an AI-enabled growth strategy-while striving to preserve trust, accountability, and social cohesion in the process.
