The Next Wave of Al - Inference Outside the Hyperscale Datacenter with Guy Currier of Futurum Group

Why It Matters

This shift changes where vendors, cloud providers and enterprises should invest in infrastructure, tools and standards—prioritizing distributed inference, edge deployment and interoperability rather than solely hyperscale training capacity. Adopting the AI compute continuum will be key to scaling real‑world AI use cases and reducing project failure rates.

Summary

Futurum Group analyst Guy Currier told an Open Compute Project panel that AI investment is shifting from large-scale training toward inference, marking a market maturation where faster growth is emerging around deploying and operating inference workloads. He noted OCP’s new “AI compute continuum” project as formal recognition that AI will span hyperscale cores to edge locations, and emphasized that fine‑tuning remains part of the training bucket even as training itself becomes more diverse and staged. Currier used a Roman‑colony analogy to argue the industry is moving from elite, centralized R&D outposts to distributed inference 'roads' that extend AI capabilities into the real world. The upshot: architects and operators must design for a continuum of hosting—cloud, neoclouds and edge—with data proximity, latency and trust controls guiding placement decisions.

Original Description

Guy Currier of The Futurum Group presented on "The Next Wave of AI," focusing on the increasing importance of inference outside hyperscale data centers. He highlighted a maturing AI market characterized by a significant shift in investment from AI training towards AI inference. This trend, supported by Futurum Group research, indicates faster growth in inference, particularly between 2025 and 2027, with fine-tuning and smaller model training also categorized under this evolving landscape. This development aligns with the Open Compute Project's new AI Compute Continuum initiative, which acknowledges the critical role of AI extending beyond traditional enterprise data centers to the furthest reaches of the edge.

Currier employed a historical analogy of the Roman Empire's expansion through "colonias" or outposts to illustrate the progression of AI deployment. He described the initial phase of AI development as being concentrated in elite, core, cloud-based environments, similar to these powerful yet contained Roman settlements. The current and future wave involves extending AI capabilities, much like building Roman roads, by distributing AI services and applications to the edge. This expansion is driven by the practical need to place AI where it offers optimal performance, considering factors such as data proximity and specific physical environment requirements, ultimately providing users and architects with enhanced control and flexibility across a continuum from core to edge.

The presentation underscored the emerging concept of "hyper-hybridity," suggesting that every edge deployment is fundamentally a hybrid one, spanning cloud, on-premise, and diverse edge locations. While the core AI market remains larger, the edge market is projected to grow substantially, nearing parallel in size by 2030. Critical enduring questions for this expansion include the necessity for robust standards, particularly given the traditional hyperscale focus of organizations like OCP. There is also a recognition of immense diversity in processing units beyond GPUs, the indispensable role of resilient networking to overcome edge discontinuities, and the intricate interplay between hardware and software in driving efficient, distributed AI solutions, including the rapid development of custom chips.

Presented by Guy Currier, Research Director, The Futurum Group. Recorded live in San Jose, California, on May 14, 2026 as part of AI Field Day 8. Watch all three Community Presentations at https://techfieldday.com/appearance/ai-field-day-8-community-presentations/ or visit https://TechFieldDay.com/event/aifd8/ to learn more.