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Latest Small Island Research Notes
Beyond the Traditional Hardware Framework: AI Infrastructure Is Forming a New System Cycle
2026-05-20
Executive Summary
This AI infrastructure cycle may not be fully understood through the traditional framework of technology hardware cycles.
First, although the market keeps discussing an AI bubble, these early bubble warnings may actually make suppliers more disciplined about capital spending and delay the point at which supply becomes excessive.
Second, KV cache is changing the architecture of AI infrastructure. AI infrastructure is no longer only a question of supply and demand for individual hardware components. It has become a system-efficiency problem shaped by compute, memory, storage, networking, power, and cooling. Bottlenecks may move across different layers of the system.
Third, this cycle of AI demand does not come entirely from mature end demand. Early buildouts by platform companies, capability competition among model companies, experimental demand from AI startups, expansion supported by capital markets, and strategic and defensive demand are all contributing to the growth of AI infrastructure.
As a result, this AI infrastructure cycle may be neither as simple as a traditional hardware cycle nor merely a straightforward long-term growth story. It looks more like a complex cycle shaped by supply discipline, platform competition, system architecture, and the validation of end demand. What deserves attention is which layer of the system becomes imbalanced first, and which layer may become the next supporting layer.
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Latest Small Island Research Notes
Beyond the Traditional Hardware Framework: AI Infrastructure Is Forming a New System Cycle
2026-05-20
Executive Summary
This AI infrastructure cycle may not be fully understood through the traditional framework of technology hardware cycles.
First, although the market keeps discussing an AI bubble, these early bubble warnings may actually make suppliers more disciplined about capital spending and delay the point at which supply becomes excessive.
Second, KV cache is changing the architecture of AI infrastructure. AI infrastructure is no longer only a question of supply and demand for individual hardware components. It has become a system-efficiency problem shaped by compute, memory, storage, networking, power, and cooling. Bottlenecks may move across different layers of the system.
Third, this cycle of AI demand does not come entirely from mature end demand. Early buildouts by platform companies, capability competition among model companies, experimental demand from AI startups, expansion supported by capital markets, and strategic and defensive demand are all contributing to the growth of AI infrastructure.
As a result, this AI infrastructure cycle may be neither as simple as a traditional hardware cycle nor merely a straightforward long-term growth story. It looks more like a complex cycle shaped by supply discipline, platform competition, system architecture, and the validation of end demand. What deserves attention is which layer of the system becomes imbalanced first, and which layer may become the next supporting layer.