Artificial Intelligence (AI) in banking has crossed an important threshold. It is no longer best understood as a set of tools that automate discrete tasks, such as document processing or customer support. AI is becoming infrastructure, deeply embedded in how banks allocate capital, manage risk, comply with regulation, and interact with customers.

Like other forms of infrastructure, it rewards scale, compounds advantages over time, and reshapes market structure in ways that are difficult to reverse.

This shift has profound implications for the global financial system. While AI is often described as a democratizing force, recent evidence suggests that it may instead deepen structural divides between global systemically important banks and domestic financial systems. The divergence is not primarily about access to algorithms.

It is about capital intensity, energy-backed compute, data ecosystems, governance velocity, and institutional learning. Global banks are reorganizing themselves around AI as a permanent operating layer. Domestic and regional institutions, even when strategically aligned, face binding constraints that limit their ability to do the same.

Power as capital

One of the least visible but most consequential drivers of divergence is the transformation of compute and electricity into economic inputs comparable to capital. Global investment in AI and data-center infrastructure is projected to exceed five trillion dollars over the next five years, with annual spending surpassing five hundred billion dollars by 2026. These investments are predicated on access to reliable, scalable power.

AI workloads are expected to push global data-center electricity demand close to one thousand terawatt-hours by 2030. At the same time, power infrastructure is under strain. In advanced economies, a large share of transmission networks is decades old, while permitting and construction timelines for new capacity routinely exceed five years. As a result, power availability is becoming a scarce resource.

Large global banks, in partnership with hyperscalers, can secure long-term access to compute through reserved cloud capacity, sovereign regions, and direct arrangements with utilities. Domestic banks are typically exposed to variable pricing, higher latency, and constrained capacity.

Over time, electricity-backed compute availability becomes a balance-sheet advantage. AI capability is no longer determined only by technical ambition, but by an institution’s ability to guarantee continuous access to power-intensive infrastructure.

Capability is defined by governance

Much of the public debate around AI competition focuses on whether institutions rely on proprietary or open-source models. In practice, this distinction is less important than the quality of access surrounding those models. Early releases, deeper evaluation tooling, custom safety layers, and rapid remediation pipelines determine how effectively AI can be deployed in regulated environments.

Global banks increasingly operate in close partnership with technology providers, allowing them to co-design evaluation frameworks, integrate explainability, and embed governance directly into model lifecycles. Domestic institutions typically receive standardized configurations optimized for broad applicability and conservative risk profiles. Access is therefore tiered. Most institutions are not excluded from AI, but their ability to shape system behavior differs materially.

This asymmetry compounds over time. Institutions that can steer models adapt them more closely to their operating context, while others must adjust workflows to fit predefined system behavior.

The result is divergence in productivity, risk management quality, and speed of innovation, even when nominal access to models appears similar.

Governance speed as competitive advantage

Regulatory compliance in the AI era is not only a matter of cost; it is a matter of time. Across jurisdictions, 2025 and 2026 mark a transition toward accountability-first AI governance, with explicit expectations around explainability, stress testing, third-party risk management, and continuous monitoring.

Institutions that can translate regulatory guidance into executable controls quickly gain a structural advantage. Large global banks increasingly use AI itself to compress compliance cycles, mapping new rules into controls, tests, and audit trails within weeks. This reduces time-to-permission for new products and allows faster iteration under supervisory oversight.

Domestic banks, often operating with smaller compliance teams and limited automation, experience regulation as a delay rather than an enabler. Manual interpretation and remediation stretch deployment timelines from weeks into months. Over time, differences in regulatory throughput become a competitive differentiator independent of customer demand or product design.

Data exhaust and the invisible learning asymmetry

Data advantage in AI extends beyond raw customer information. AI systems generate large volumes of operational exhaust, prompt distributions, error patterns, human override logs, edge-case behaviors, and remediation histories. This second-order data is critical for improving models, workflows, and safety mechanisms.

Institutions operating at scale generate more of this exhaust and can close learning loops faster.

Smaller institutions, interacting with shared platforms, contribute to ecosystem learning but often lack ownership or reuse rights over the insights derived from their interactions. Even when sensitive data remains local, patterns of use become a source of competitive learning asymmetry.

Over time, this dynamic allows global banks to refine systems continuously, while domestic institutions depend on externally updated tools. The gap is not merely technological; it is institutional, affecting how quickly organizations learn from their own operations.

The hollowing of domestic credit

A quieter but potentially more consequential risk arises from the convergence of domestic institutions on standardized AI models for credit, collections, and fraud. Vendor-provided systems optimize for average performance across markets, not local nuance. As adoption spreads, decision- making becomes correlated.

During periods of stress, correlated models can amplify procyclicality, leading to synchronized credit tightening even when local conditions differ. Over longer horizons, domestic banks may retreat from complex segments, small enterprises, rural borrowers, informal workers, because standardized models struggle to price these risks accurately.

The outcome is not an abrupt crisis, but a gradual hollowing of domestic credit intermediation. Aggregate inclusion metrics may improve, while relationship-based lending erodes. This has implications for local economic resilience and the effectiveness of monetary policy transmission.

Capital allocation

These dynamics are reinforced by differences in capital allocation. Global banks routinely invest seven to ten percent of revenue in technology, treating AI as non-discretionary infrastructure. In many emerging markets, banks allocate closer to three to five percent, with a large share consumed by maintaining legacy systems.

When the majority of an IT budget is dedicated to running the bank, transformation becomes structurally constrained. Legacy platforms demand ongoing maintenance, delaying modernization and increasing the cost and risk of AI deployment. This creates a self-reinforcing loop: limited transformation capacity slows AI adoption, which in turn weakens returns and constrains future investment.

Institutional learning gaps

AI-driven skill requirements in finance are evolving rapidly, with wage premiums for advanced AI roles rising sharply. Global banks can absorb these costs, building multidisciplinary teams that integrate research, engineering, product, and risk expertise.

Domestic institutions rely more heavily on vendors or smaller internal teams, slowing institutional learning. Over time, this affects not only deployment speed but governance quality, as fewer internal stakeholders fully understand system behavior, limitations, and risks.

Collective AI as a partial bridge

Despite these pressures, AI also offers mechanisms to narrow the gap if approached collectively.

Federated learning allows institutions to share intelligence on fraud and financial crime without pooling sensitive data. Synthetic data enables banks with limited historical depth to train systems and staff on rare but critical scenarios. Agentic AI systems, when deployed with human oversight, can reduce compliance and risk-management costs while improving quality.

The constraint is not technical feasibility but coordination. Competitive incentives encourage institutions to internalize advantage, while systemic stability depends on shared solutions in non-differentiating domains. Fraud prevention, operational resilience, and regulatory reporting are natural candidates for cooperative AI infrastructure, while product design and client relationships remain areas for competition.

The true measure of success will not be the sophistication of any single institution’s models, but the resilience, inclusivity, and adaptability of the global financial system as a whole. – editor@nrifocus.com

– The writer is Data Engineering Tech Lead at USAA. He has dedicated his career to developing solutions for real-time fraud detection, Anti-Money Laundering data platforms, and Explainable AI systems