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Nazrin Abdul

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On February 10, the Azerbaijan and the US governments signed a Charter on Strategic Partnership, identifying Artificial Intelligence (AI) and Digital Infrastructure as one of the key pillars of future cooperation.

To better understand the long-term technological and regulatory implications of this partnership, we spoke with Etibar Aliyev, an AI expert with a background in computational analytics and informatics. Aliyev works on developing and optimizing large language models at Google and Meta, contributes technical expertise to the California AI Audit Bill and the International Association of Algorithmic Auditors (IAAA), and is a senior member of the IEEE. He is also the author of several peer-reviewed studies on AI applications in immersive environments, adaptive narratives, and social network algorithms.

Building a regional AI hub: Infrastructure, regulation, and the “model factory” concept

The Charter emphasizes expanding AI cooperation, including the development of AI data centers in Azerbaijan in collaboration with the private sector. Beyond symbolic cooperation, this signals a potential ambition: positioning Azerbaijan as a regional AI and digital infrastructure hub.

Q: The Strategic Partnership Charter highlights plans to expand AI cooperation and develop AI data centers in Azerbaijan in collaboration with the private sector. From your experience working on large language models at Google and Meta, what technical, regulatory, and infrastructure prerequisites are essential for Azerbaijan to become a competitive regional hub for AI data centers and advanced model development?

A: From the vantage point of building and stress-testing frontier-scale language models, the countries that become credible regional AI hubs usually get three layers right at the same time: dependable power, dependable networks, and dependable rules. On the infrastructure side, the non-negotiable is energy predictability - stable baseload, a realistic path to redundancy (N+1 at minimum for critical systems), and a power market structure that can support long-term contracts so operators can price capacity sensibly. The second is connectivity: multiple physically diverse fiber routes, low-latency peering, and clear cross-border transit agreements so training and inference traffic doesn’t bottleneck at the edge. The third is “datacenter-grade operations”: a local ecosystem of facilities engineers, SREs, hardware technicians, and vendors who can support high-density GPU clusters, liquid cooling, and rapid component turnaround without week-long import delays.

On the technical side, competitiveness today is less about owning “a big model” and more about building a reliable model factory. That means strong data engineering and governance, serious evaluation harnesses, and the ability to fine-tune and deploy models repeatedly with measurable improvements and known failure modes. It also means setting up secure compute enclaves for sensitive workloads (government, energy, telecom, defense-adjacent) so that not everything has to run in a single risk tier. Finally, for Azerbaijan specifically, the “space industry” angle is a strategic advantage if it’s translated into high-quality geospatial and remote-sensing pipelines - curated national datasets, standardized metadata, and licensing frameworks - because those become durable assets for local model development and downstream products.

Regulatorily, what matters most is clarity and speed. Investors and hyperscalers can work with strict requirements; they struggle with ambiguity. Azerbaijan can be attractive if it provides a crisp regime for data localization versus cross-border transfer, a predictable privacy baseline, and a risk-tiered security framework aligned with widely recognized standards. If you want advanced model development to happen locally, you also need procurement and compliance pathways that make it possible for banks, telcos, and public agencies to adopt domestic AI services without taking on unbounded legal risk. In practice, that looks like well-defined accountability for model outputs, transparent incident reporting expectations, and a credible audit and certification ecosystem that can be trusted regionally. This aligns closely with the kind of evaluation and accountability work I’ve been involved in around LLM benchmarking and algorithmic auditing.

Aliyev’s emphasis on “dependable power, networks, and rules” reframes AI infrastructure as a systems-level challenge rather than a purely technological one. His “model factory” concept also suggests that long-term competitiveness will depend less on headline-grabbing model launches and more on operational discipline, evaluation standards, and regulatory predictability.

De-risking innovation: Governance, auditing, and venture confidence

The Charter proposes joint R&D instruments, innovation bridge platforms, and sector-focused AI and cybersecurity initiatives designed to attract private capital and reduce early-stage risk.

Q: Based on successful international models you have observed, what kind of governance and auditing frameworks should be built into these mechanisms to ensure both innovation and algorithmic accountability from the outset?

A: The governance pattern that tends to work internationally is to separate “money decisions” from “technical truth.” In other words, you want an independent technical evaluation function that cannot be overridden by program managers who are incentivized to show quick wins. If a joint R&D instrument is meant to de-risk early-stage tech, it should have a built-in stage-gating model where funding increases only when teams demonstrate measurable progress against predefined metrics: reproducibility of results, security posture, documented model limitations, and evidence that performance generalizes beyond a handpicked benchmark.

Auditing frameworks should be “continuous,” not ceremonial. For AI and cybersecurity initiatives, that means requiring a living model card and system card that evolves with every major release: training and fine-tuning provenance, data rights and consent status, evaluation results across languages and demographic slices relevant to the region, red-team findings, and a rollback plan when issues appear in production. The key governance move is to standardize these artifacts across all funded projects so investors can compare opportunities and regulators can assess risk consistently. When this is done well, it actually accelerates venture investment because it compresses diligence: VCs don’t need to guess whether a team is hiding technical debt.

Algorithmic accountability also needs teeth without becoming a bureaucratic tax. The clean way to do this is to define a baseline audit scope for every project (privacy, security, robustness, bias/fairness, explainability where relevant) and then add deeper requirements only for high-impact use cases like hiring, credit, critical infrastructure, or public services. A joint U.S.- Azerbaijan structure can make this credible by creating a shared auditor accreditation pathway and conflict-of-interest rules for evaluators, so the same actors aren’t simultaneously building, scoring, and certifying the system. That “independence by design” is the difference between a trust-building innovation bridge and a grant program that produces prototypes nobody can safely deploy.

His argument that accountability can accelerate, rather than hinder, venture investment is particularly notable. By standardizing evaluation artifacts and audit requirements, Azerbaijan and the U.S. could potentially reduce uncertainty for investors - transforming governance into a competitive advantage.

Talent, connectivity, and avoiding technological dependency

Human capital development and cross-border digital connectivity, including Trans-Caspian infrastructure, are also central to the Charter.

Q: What skills and institutional capacities should Azerbaijan prioritize to build a sustainable AI workforce, and how can collaboration with U.S. tech ecosystems accelerate this process without creating technological dependency?

A: If Azerbaijan wants a sustainable AI workforce, it should prioritize the skills that make models dependable in the wild, not just impressive in demos. The most persistent gap I see is the middle layer between research and production: people who can build data pipelines, run rigorous evaluations, manage drift, harden systems, and keep costs under control. That layer includes Machine-Learning (ML) engineers, data engineers, applied scientists, SREs, security engineers, and product-minded technical leads who understand both model behavior and operational constraints. Alongside this, the country should deliberately grow a smaller but high-caliber cohort in specialized areas, hardware-aware training optimization, privacy engineering, adversarial ML, and AI safety evaluation, because those become national multipliers once the datacenter and compute ecosystem matures.

Institutionally, the fastest accelerator is to create a few anchor labs and “teaching hospitals” for AI: places where students, startups, and government technologists work on real deployments with real constraints, under strong mentorship, with shared infrastructure and shared evaluation tooling. Connectivity investments like Trans-Caspian infrastructure matter here because they reduce latency, improve redundancy, and make it feasible to run hybrid architectures where some services sit locally while collaborating across borders for research, benchmarking, and disaster recovery.

Collaboration with U.S. tech ecosystems can speed this up dramatically if it’s structured around capability transfer rather than consumption. The healthy model is joint programs where Azerbaijani teams co-develop evaluation frameworks, safety tooling, and domain datasets, and learn the operational playbook, rather than simply licensing black-box systems. You avoid dependency by insisting on three things in partnerships: local ownership of critical datasets and labeling pipelines, local capacity to reproduce key results, and contractual rights to audit model behavior and security posture. When those are built in, “collaboration” becomes an engine for autonomy rather than a path to lock-in.

The expert’s focus on the “middle layer” of AI talent highlights a common gap in emerging ecosystems: operational excellence. His emphasis on capability transfer and local dataset ownership reflects a strategic concern about digital sovereignty within global AI partnerships.

Open innovation vs. technology protection: Finding the balance

The Charter also underscores joint research, commercialization, voluntary technology transfer, and legal safeguards.

Q: How can Azerbaijan and the United States balance open innovation with intellectual property protection, security concerns, and ethical AI governance?

A: Balancing open innovation with IP protection and security is mostly a matter of being explicit about what is open, what is shared, and what is controlled, and then enforcing that through process. In joint research, you can keep a lot of momentum by publishing methods, benchmarks, and evaluation findings while keeping sensitive assets protected. A practical approach is to classify projects and artifacts into tiers. Low-risk work can be open by default. Higher-risk work requires controlled access, secure environments, and formal review before release.

On the U.S.- Azerbaijan axis, technology protection becomes easier when both sides adopt a common compliance language. That means aligning around internationally recognized security and risk standards, using shared documentation templates for AI systems, and establishing joint red-teaming protocols so vulnerabilities are discovered before commercialization. For intellectual property, the best balance is to clearly separate background IP, foreground IP, and usage rights. When those are vague, partnerships slow down; when they’re precise, collaboration speeds up.

Ethical AI governance needs to be implemented as engineering practice, not policy theater. You get there by requiring measurable evaluations, traceable data provenance, and credible third-party audits for high-impact deployments, paired with incident response processes that treat AI failures like any other reliability or security issue. The most durable setup is one where accountability artifacts are produced routinely as part of the development lifecycle, and regulators focus on verifying process integrity and risk controls rather than trying to “judge intelligence” directly.

President Aliyev’s closing remarks reinforce a central theme: governance must be operationalized. For the Azerbaijan–U.S. partnership to succeed in AI, legal language alone will not suffice; durable cooperation will depend on shared standards, enforceable processes, and embedded accountability within engineering practice itself.