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DIGITIMES Intelligence: AI’s Next Battleground Is Moving Beyond Chips to the Connections Between Them
TAIPEI, Sept. 2, 2026 /PRNewswire/ — The global race to build artificial intelligence infrastructure is entering a new phase. For the past several years, much of the competition has centered on securing the most powerful AI accelerators. But as hyperscalers pour billions of dollars into data centers while developing more of their own silicon, another strategic question is moving to the forefront: how to efficiently connect an increasingly diverse mix of AI chips and turn them into computing systems that can operate at massive scale.
The scale of investment makes that question increasingly important. McKinsey estimates that nearly $7 trillion in global data center capital expenditure could be required by 2030, including about $5.2 trillion for facilities supporting AI workloads. Under its continued-momentum scenario, AI-related data center capacity demand could rise to approximately 156 gigawatts by 2030, roughly 3.5 times its 2025 level. As spending expands across accelerators, networking, storage, power and cooling, the architecture connecting these systems is becoming a larger part of the infrastructure equation.
At the same time, the AI silicon landscape itself is becoming more diverse. Hyperscalers are increasingly combining general-purpose GPUs with custom accelerators optimized for their own workloads. Google, Amazon, Microsoft and Meta have all pursued proprietary AI silicon strategies, while partnerships between cloud companies and semiconductor suppliers continue to deepen. The trend was underscored again in August, when Google expanded its custom AI chip partnership with Marvell — another sign that hyperscalers are seeking greater control and diversification across their AI hardware stacks.
Against this backdrop, a new DIGITIMES Intelligence report finds that AI infrastructure is moving toward a multi-architecture environment, where no single chip platform or interconnect standard is likely to serve every workload. As cloud service providers deploy their own AI ASICs alongside GPUs, competition is intensifying around AI fabric — the interconnect systems that allow accelerators, nodes, racks and clusters to operate together as a unified computing infrastructure.
The report identifies two critical layers of this competition. Scale-up handles high-speed, low-latency connections among accelerators within a node or rack, while scale-out connects nodes, racks and clusters across a larger computing environment. As new AI chip architectures proliferate, each introduces different requirements for bandwidth, latency, power efficiency and interoperability — making the coordination between these two layers increasingly important.
