Hardware cycles are accelerating, regulations are multiplying, and costs are rising. A framework for AI infrastructure decisions that last through 2031.
Most AI infrastructure decisions are made for the next budget cycle. The consequences often last far longer.
The pace of change is accelerating. NVIDIA is now operating on an annual AI platform roadmap, with Vera Rubin expected in 2026, Rubin Ultra in 2027, and Feynman planned for 2028. Infrastructure purchased today may face significant competitive pressure from newer generations within just a few years.
At the same time, regulatory requirements continue to expand. The EU AI Act reaches full applicability in 2026, while DORA, NIS2, and the Data Act are adding new expectations around governance, auditability, resilience, and data control.
The scale of investment is equally unprecedented. Goldman Sachs estimates roughly $7.6 trillion in cumulative AI infrastructure spending between 2026 and 2031, covering chips, data centers, and power infrastructure. Annual spending is expected to grow from approximately $765 billion in 2026 to $1.6 trillion by 2031.
In an environment where technology, regulations, and economics are all changing simultaneously, flexibility becomes a strategic advantage.
Organizations built on proprietary platforms will spend more time adapting to vendor decisions, pricing changes, and platform limitations. Organizations built on open infrastructure can evolve alongside the market.
The goal is not to predict what AI infrastructure will look like in 2031. The goal is to build on foundations that can adapt when it changes. OpenStack provides infrastructure control. Kubernetes provides workload portability. Together, they create the flexibility needed to navigate whatever comes next.
§1 Hardware Changes Faster Than Infrastructure Contracts
NVIDIA's AI roadmap is now operating on an annual cadence. Vera Rubin is expected in 2026, Rubin Ultra in 2027, and Feynman in 2028. As a result, organizations can expect multiple hardware generations to emerge during the lifespan of a typical infrastructure contract.
The challenge is that GPU infrastructure is often purchased through one to three year commitments tied to specific providers, instance types, or hardware generations. By the time those agreements expire, the market may have shifted significantly in terms of performance, efficiency, and cost.
This creates a planning problem. Infrastructure decisions made today must support workloads for years, while the underlying hardware landscape continues to evolve. The question is no longer which GPU generation to choose. It's how quickly your organization can adopt the next one.
A long-term AI strategy should not depend on a specific accelerator, instance type, or cloud provider. It should be built on infrastructure that can evolve as the hardware changes.
This is where abstraction matters. OpenStack provides a consistent infrastructure layer across compute, networking, and accelerators through open APIs. As new hardware becomes available, organizations can integrate and adopt it without redesigning the platforms and workflows built above it.
For a closer look at how infrastructure decisions can become long term platform dependencies, read GPUs Are the New Lock-In Strategy.
§2 Regulations Will Continue to Evolve
Technology is changing rapidly, but regulation is moving in the same direction.
The EU AI Act reaches full applicability in 2026, while DORA, NIS2, and the Data Act are introducing new requirements around governance, resilience, security, auditability, and data control. Together, these frameworks place increasing emphasis on understanding where data resides, how systems operate, and how organizations maintain control over critical infrastructure.
The challenge is that compliance requirements rarely remain static. New regulations tend to build on existing ones, adding reporting obligations, security requirements, and operational expectations over time. Organizations should assume that the regulatory environment of 2031 will be more demanding than it is today.
This has important implications for infrastructure strategy. Platforms that limit visibility, portability, or operational control can make future compliance efforts more difficult and costly.
Platforms built on open standards and transparent control planes provide greater flexibility as requirements evolve.
A long-term AI strategy should not be designed around today's regulations alone. It should be built to accommodate the regulations that have not yet been written.
For a detailed breakdown of how current regulations converge on infrastructure decisions, read Sovereign by Architecture: Building AI Infrastructure for the EU AI Act. For broader context on why sovereignty is becoming a strategic imperative, read Digital Sovereignty and AI: Why Governments Are Betting on Open Infrastructure.
§3 Costs Will Continue to Change
AI infrastructure is becoming one of the largest technology investments many organizations make. At the same time, the economics of running AI workloads remain highly dynamic.
Demand for GPUs, power, networking, and data center capacity continues to grow, while organizations are deploying increasingly compute-intensive workloads. As a result, infrastructure costs are under greater scrutiny than ever before.
The challenge is not simply that costs may rise. It is that costs can change rapidly across providers, regions, hardware generations, and deployment models. What appears to be the most economical option today may not remain so over the lifespan of an application or platform.
This is particularly important as AI moves from experimentation to production. Unlike training workloads, production inference often runs continuously and scales with usage, making long-term infrastructure efficiency a critical consideration.
A long-term AI strategy should not assume that today's pricing models will remain unchanged. It should be built around visibility, portability, and flexibility, allowing organizations to place workloads where the economics make the most sense as requirements evolve.
The goal is not to predict future costs. It is to maintain the ability to adapt when they change.
For a direct comparison of hyperscaler costs versus open infrastructure, read The Real Cost of Running AI on Hyperscalers vs. Open Infrastructure. For more on how pricing structures quietly erode cost control, read What Your Cloud Provider Doesn't Want You to Think About.
§4 Workloads Will Continue to Evolve
The AI workloads running on your infrastructure today are unlikely to be the same workloads running three or five years from now.
The AI landscape is changing rapidly. New model architectures, deployment patterns, and operational requirements are emerging at a pace that makes long-term forecasting difficult. Organizations that began with experimentation are now supporting production inference, while others are exploring agents, multimodal systems, and increasingly distributed AI workflows.
Each shift introduces different infrastructure requirements. Some workloads prioritize raw compute performance. Others require low latency, high availability, efficient resource sharing, or closer integration with data and applications.
The challenge is that infrastructure often outlives the assumptions it was designed around. Platforms optimized for a specific workload pattern can become constraints when requirements change.
A long-term AI strategy should not be built around today's models or deployment patterns. It should be built around adaptability, allowing teams to support new workloads without redesigning the underlying platform.
Kubernetes provides flexibility at the orchestration layer through portable, declarative workloads. OpenStack extends the same principle to infrastructure, creating a foundation that can evolve as applications, models, and requirements change.
For a closer look at how training and inference have different infrastructure profiles, read AI Workloads on Kubernetes: Training vs. Inference Infrastructure. For more on what production AI workloads demand from infrastructure, read Why Half of AI Projects Never Leave Pilot.
§5 Principles for Long-Term AI Infrastructure
Hardware will change. Regulations will evolve. Costs will shift. Workloads will look different than they do today.
While it's impossible to predict exactly how the landscape will develop, organizations can make infrastructure decisions that preserve flexibility as requirements change.
Five principles can help guide those decisions:
1. Open standards over proprietary dependencies
Open standards reduce reliance on any single vendor and make it easier to integrate new technologies, platforms, and deployment models over time.
2. Abstraction over hardware specificity
Hardware generations evolve quickly. Infrastructure should provide a consistent operational model that allows organizations to adopt new hardware without redesigning the platforms built on top of it.
Hardware will change. Regulations will evolve. Costs will shift. Workloads will look different than they do today.
While it's impossible to predict exactly how the landscape will develop, organizations can make infrastructure decisions that preserve flexibility as requirements change.
Five principles can help guide those decisions:
1. Open standards over proprietary dependencies
Open standards reduce reliance on any single vendor and make it easier to integrate new technologies, platforms, and deployment models over time.
2. Abstraction over hardware specificity
Hardware generations evolve quickly. Infrastructure should provide a consistent operational model that allows organizations to adopt new hardware without redesigning the platforms built on top of it.
3. Portability over platform dependence
Workloads should be able to move between environments as business, technical, or regulatory requirements change. Portability preserves options and reduces migration risk.
4. Control over unnecessary complexity
Organizations should retain visibility and control over the infrastructure layers that influence cost, performance, security, and compliance. The goal is not to manage everything manually, but to maintain the ability to make informed decisions when requirements change.
5. Auditability by design
Visibility and transparency become increasingly important as operational and regulatory expectations grow. Infrastructure should provide the information needed to understand, validate, and govern how systems operate.
These principles are not tied to a specific technology or deployment model. They are design choices intended to maximize adaptability in an environment where change is inevitable.
For more on how the shift from "cloud first" to "control first" is playing out across the industry, read "Cloud First" Is Becoming "Control First" — Here's What Changed.
§6 How OpenStack + Kubernetes Deliver Long-Term Flexibility
Every principle in this framework points to the same architectural foundation: open infrastructure that separates workload control from infrastructure control, and both from hardware.
OpenStack abstracts infrastructure from hardware generations. When a new GPU ships, OpenStack manages it through the same open APIs. Compute allocation, storage placement, networking, and identity don't change because the silicon beneath them changed. New hardware slots in. The stack above it continues.
Kubernetes abstracts workloads from infrastructure. Training jobs, inference endpoints, and agent workloads are defined declaratively through CNCF standards. When infrastructure changes, new region, new provider, new deployment model, workloads move without re-engineering.
Together, they create an architecture designed to outlast any single hardware cycle, any single regulation, and any single cost model.
Atmosphere by VEXXHOST delivers this as a production-ready platform. Upstream OpenStack and CNCF-certified Kubernetes, no proprietary forks, no vendor extensions. GPU passthrough, MIG, and vGPU for flexible compute across current and future hardware. Ceph for scalable storage. SR-IOV and DPDK for high-performance networking. Deploy on-premise, colocation, or hosted, and change that decision later without rebuilding.
The platform is built to evolve. When Vera Rubin ships, Atmosphere supports it. When new regulations take effect, the auditable stack is already compliant. When costs shift, you move workloads where the economics make sense, because nothing in the architecture prevents it.
For teams that need this foundation without building an infrastructure ops team from scratch, VEXXHOST offers fully managed operations, so the platform stays current while your team focuses on what runs on top of it.
For a complete overview of the platform, read The Complete Guide to Managed OpenStack with Atmosphere.
Conclusion
The infrastructure decisions you make this quarter will still be shaping your options in 2031. Hardware will cycle through multiple generations. Regulations will expand. Costs will shift. Workloads will evolve into forms that don't exist yet.
The organizations that build on rigid, provider-locked architectures will spend the next five years reacting. The ones that build on open, flexible foundations will adapt.
Five principles. One architectural foundation. OpenStack for infrastructure control. Kubernetes for workload portability. Atmosphere for both, built to survive whatever the next five years bring.
Explore Atmosphere — AI infrastructure built for the long term.
