Scope
- Geography: global
- Scope: compute and acceleration substrate, storage fabric, sovereignty, security, and confidential computing, data mobility and locality, and supporting platform and operational technologies
Contents
In this report, we outline:
- The evolution of neoclouds as AI-first infrastructure platforms emerging in response to growing AI workload intensity, GPU demand, cloud cost inefficiencies, and the need for specialized infrastructure optimized for large-scale AI training and inference
- The full neocloud infrastructure stack, including accelerator-dense compute, high-performance interconnect fabrics, AI-optimized storage, confidential computing, sovereignty controls, and intelligent data mobility architectures that collectively enable scalable AI-native cloud environments
- Next-generation infrastructure technologies such as GPUs with HBM, AI ASICs, CXL-based memory pooling, NVLink/NVSwitch, RoCEv2, silicon photonics, NVMe-over-Fabrics, confidential computing, federated learning, and geo-replicated edge-aware data systems, along with their maturity, benefits, and operational challenges
- Innovation dynamics and ecosystem evolution through patent trends, strategic partnerships, regional R&D hotspots, private and public funding activity, and the changing roles of GPU providers, networking vendors, storage platforms, orchestration ecosystems, and sovereign AI infrastructure initiatives
- The market potential and economic trajectory of neoclouds by examining AI infrastructure spending growth, GPU scarcity, power and cooling constraints, evolving capital models, regional market opportunities, and the transition from opportunistic GPU marketplaces to industrial-scale AI infrastructure operators
- The impact of regulations, data sovereignty mandates, export controls, cybersecurity frameworks, cloud portability requirements, and AI governance policies in shaping the future architecture, deployment models, and regional fragmentation of neocloud ecosystems
- A forward-looking perspective on the evolution of neoclouds toward autonomous, policy-aware AI infrastructure platforms while highlighting key risks related to operational complexities, energy availability, heterogeneous accelerator ecosystems, autonomous control planes, and systemic infrastructure interdependencies