The Companies That Win No Matter Which AI Model Wins
See how NVIDIA, TSMC, cloud platforms and infrastructure suppliers benefit across AI models, and what their advantages mean for enterprise buying decisions.
AI infrastructure
China’s AI Counteroffensive: Model Parity Under Silicon Constraints
Evaluate Chinese AI models for enterprise use. Compare Qwen, DeepSeek, Kimi, GLM, Seed and Huawei across capability, silicon constraints and deployment risk.
The Compute Arms Race: Stargate, Colossus, TPUs and the Gigawatt Battlefield
Compare Stargate, Colossus and Google TPUs. Separate AI compute promises from usable capacity with evidence on power, networks, workload fit and recovery.
The AI Power Stack: Who Is Winning the AI Race?
Explore the AI power stack, compare leading ecosystems, and see why compute, distribution, agent control, and exit options matter beyond model benchmarks.
Why Mirantis k0rdent AI Is the AI Factory Operating Layer the Market Has Been Missing
TL;DR The AI infrastructure market has spent too much time treating GPU acquisition, Kubernetes deployment, workload scheduling, model serving, and platform governance as separate purchases. Enterprises and neoclouds do not experience them separately. They experience the gaps between them, where driver mismatches, operator ordering, network configuration, tenant policy, lifecycle ownership, and support boundaries turn expensive … Explore: Why Mirantis k0rdent AI Is the AI Factory…
AI Power Is Now a Business Capacity Decision: What CEOs and CIOs Need to Know About Megawatts, Cooling, and Community Approval
TL;DR AI infrastructure capacity is no longer measured credibly by GPU count alone. A reserved accelerator becomes usable production capacity only when the organization can also provide rack power, cooling, network and storage throughput, facility headroom, grid or on-site generation, permits, water strategy, operational support, and community acceptance. The practical CIO model is to treat … Explore: AI Power Is Now a Business Capacity Decision:…
GPUs Are Not a Cloud: Why Neoclouds Need Vendor Neutral AI Infrastructure Orchestration
TL;DR Neoclouds may begin by selling access to scarce GPU capacity, but long-term differentiation requires more than racks, drivers, and a booking portal. A production AI cloud must convert bare-metal servers, virtual machines, GPU pools, storage, networks, and external cloud resources into secure, repeatable, tenant-aware services. The missing layer is vendor-neutral AI infrastructure orchestration. It … Explore: GPUs Are Not a Cloud: Why Neoclouds Need…
Private AI Cloud vs. Sovereign Cloud vs. Neocloud: A Practical Enterprise Guide
TL;DR Private AI cloud, sovereign cloud, and neocloud are not three interchangeable names for the same infrastructure model. A private AI cloud is designed around organizational control of AI data, models, infrastructure, identity, and operations. A sovereign cloud is designed around legal jurisdiction, operational autonomy, data and key control, supply-chain constraints, and continuity under a … Explore: Private AI Cloud vs. Sovereign Cloud vs. Neocloud:…
Your AI Strategy Is Really a Modernization Strategy: What CIOs Must Fix Before Scaling AI
TL;DR Enterprise AI readiness is not primarily determined by which model, copilot, or agent platform an organization selects. It is determined by how much of the enterprise can be safely exposed through trusted data, supported APIs, controlled identities, observable workflows, and resilient infrastructure. CIOs do not need to modernize every legacy application before deploying AI. … Explore: Your AI Strategy Is Really a Modernization Strategy:…