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Latest Small Island Research Notes
Why New AI Demand Still Often Flows to the NVIDIA Ecosystem
2026-04-04
Executive Summary
The AI compute market is becoming increasingly diverse. Large cloud providers continue to push forward with in-house ASIC and XPU development, and the number of alternatives to NVIDIA keeps growing. In theory, new AI demand should become more evenly distributed across different architectures, rather than continuing to concentrate in the NVIDIA ecosystem.
But when several recent signals are viewed together, the key question may not simply be who has compute. It may be who can bring new supply online the fastest when the market suddenly needs more compute. As test-time scaling, reasoning, and AI agents continue to develop, AI compute demand is becoming more immediate, more irregular, and more concentrated in inference workloads. This suggests that what the market is starting to lack may not just be total compute, but incremental compute that can be quickly mobilized and put to work right away.
From this perspective, not all compute is equally well suited to absorb new demand. In-house ASICs and XPUs still matter in terms of cost, power efficiency, and long-term strategic autonomy, but they are often designed to serve internal workloads first. Their deployment plans and supply chain arrangements also tend to follow longer-term planning, which may make them less suitable for handling sudden increases in external demand. By contrast, the NVIDIA ecosystem often becomes a primary absorber of new demand not only because of chip performance and CUDA, but also because of its more mature supply chain system, cloud partner network, system integration capabilities, and deployment base.
If this direction continues, the market’s understanding of the ASIC substitution path may also need to change. The more important question may not simply be whether ASICs can replace NVIDIA, but under what demand conditions they can do so. At the same time, this suggests that NVIDIA’s advantage does not come from technology alone. The importance of orchestration and deployment capability is also rising. In the future, competition in AI infrastructure may be shaped not only by who has the stronger chips, but also by who is better able to turn technology into supply that can be mobilized immediately.
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Latest Small Island Research Notes
Why New AI Demand Still Often Flows to the NVIDIA Ecosystem
2026-04-04
Executive Summary
The AI compute market is becoming increasingly diverse. Large cloud providers continue to push forward with in-house ASIC and XPU development, and the number of alternatives to NVIDIA keeps growing. In theory, new AI demand should become more evenly distributed across different architectures, rather than continuing to concentrate in the NVIDIA ecosystem.
But when several recent signals are viewed together, the key question may not simply be who has compute. It may be who can bring new supply online the fastest when the market suddenly needs more compute. As test-time scaling, reasoning, and AI agents continue to develop, AI compute demand is becoming more immediate, more irregular, and more concentrated in inference workloads. This suggests that what the market is starting to lack may not just be total compute, but incremental compute that can be quickly mobilized and put to work right away.
From this perspective, not all compute is equally well suited to absorb new demand. In-house ASICs and XPUs still matter in terms of cost, power efficiency, and long-term strategic autonomy, but they are often designed to serve internal workloads first. Their deployment plans and supply chain arrangements also tend to follow longer-term planning, which may make them less suitable for handling sudden increases in external demand. By contrast, the NVIDIA ecosystem often becomes a primary absorber of new demand not only because of chip performance and CUDA, but also because of its more mature supply chain system, cloud partner network, system integration capabilities, and deployment base.
If this direction continues, the market’s understanding of the ASIC substitution path may also need to change. The more important question may not simply be whether ASICs can replace NVIDIA, but under what demand conditions they can do so. At the same time, this suggests that NVIDIA’s advantage does not come from technology alone. The importance of orchestration and deployment capability is also rising. In the future, competition in AI infrastructure may be shaped not only by who has the stronger chips, but also by who is better able to turn technology into supply that can be mobilized immediately.