As we look ahead to 2026, the rapid evolution of AI continues and has redefined expectations for performance, security and efficiency. We know that future growth in the computing industry will be driven by megatrends requiring both increased power and efficiency, that it will involve a complex mix of AI and software systems, and that 2026 will be another pivotal year. From the chip to the data center, innovation is accelerating, and demand and spending levels are expected to be even higher than some earlier forecasts predicted. Four trends in particular stand out as the forces that will define AI infrastructure in the year ahead.

1. Memory Safety Becomes a Core Component of Infrastructure Design
Memory safety is quickly becoming a primary design requirement. The scale and sensitivity of today’s AI workloads mean that vulnerabilities once considered niche can now have widespread impact. As organizations run many high-value models concurrently, the risks of memory corruption, data leakage and exploitation increase sharply.

While many are just beginning to take the early steps to address the demand for memory safety, at Ampere®, we anticipated this shift and built memory tagging into our products long before it became a broader industry focus. In 2026, we expect memory-safe compute to move from an optional enhancement to a standard requirement, particularly for mission-critical AI operations. This year marks a turning point as memory integrity becomes a core expectation for any infrastructure built for AI at scale.

2. Enterprise AI Expands with a Broad Set of Infrastructure Needs
The move toward enterprise-driven AI Compute accelerated in 2025 and will continue in 2026. Organizations that tested distributed models last year are now expanding them into fully operational environments. Rising AI demands, the need for performance consistency, long-term cost control and stricter data requirements are pushing enterprises to rely more on infrastructure they directly control. Instead of depending exclusively on large, centralized clouds, companies are also building out regional data centers, colocation footprints and specialized on-prem deployments that are optimized for AI inference and emerging agent workloads. This reflects a more balanced approach in which enterprises blend cloud capabilities with dedicated compute resources they operate directly to place workloads where they run most efficiently and predictably.

In 2026, we expect this blended strategy to mature as organizations refine infrastructure models that use both cloud and owned compute in complimentary ways, allowing them to meet rising AI demands with greater stability and flexibility.

3. AI Workloads Diversify and Demand a Broader, More Heterogeneous Portfolio
Enterprises are rapidly expanding the range of AI workloads they run in production. According to a recent McKinsey survey, more than two-thirds of respondents say their organization is using AI in more than one function. This expanding variety highlights the reality that AI workloads cannot be served effectively by a single type of compute. Each category of model and even each individual component of a model has distinct needs around memory, latency, cost and throughput.

Agentic systems in particular accelerate this shift because they involve multiple tasks working together with different resource profiles, making it harder to reply on a single type or hardware or uniform infrastructure footprint. In 2026, heterogeneous compute becomes a defining characteristic of enterprise AI infrastructure as enterprises adapt to the widening spectrum of workloads they need to support and the mixed nature of the tasks required within the workload.

4. AGI Advances Quickly, Requiring Continued Investment in Both Compute and Power
Artificial General Intelligence (AGI) — highly autonomous systems that outperform humans at most economically viable work — is approaching faster than many expected. Its progression is reshaping the scale and intensity of infrastructure planning. Regardless of how long it takes, reaching AGI will require significant compute power, efficiency and energy solutions. The models emerging today require far more sustained compute than previous generations, along with reliable access to the power needed to operate them.

What began as a rapid uptick in investment throughout 2025 is not slowing down in 2026. Instead, organizations are preparing for a future in which AGI-level capabilities demand significantly larger and more efficient compute fleets. This next phase of AI development brings a new level of urgency as enterprises and cloud providers expand capacity to support larger models, faster iteration cycles and new forms of agentic and autonomous systems. In 2026, the race is about securing the compute and power infrastructure required to keep pace with an increasingly advanced generation of AI systems.