The shift to custom silicon
Hyperscalers are designing purpose-built AI chips to solve two problems: Nvidia's supply constraints and eye-watering GPU costs. AWS now ships Trainium for training and Inferentia for inference. Google's been running TPUs since 2015. OpenAI partnered with Broadcom and TSMC for custom accelerators.
The business case is straightforward. TrendForce projects custom ASIC shipments from cloud providers will grow 44.6% in 2026, while GPU growth hits 16.1%. The AI infrastructure market is tracking toward $450 billion by 2032.
AWS has been aggressive here. Graviton processors (ARM-based, custom-designed) deliver 40% better price-performance than comparable x86 instances for many database workloads. The company partnered with Intel for AI chips on the 18A node. Migration tools like Graviton Porting Advisor and the Savings Dashboard help enterprises assess ROI before moving workloads.
The real bottleneck isn't wafers
TSMC flags advanced packaging and high-bandwidth memory as the actual constraints, not chip fabrication. That's why clouds are diversifying: AWS to Intel foundries, others exploring AMD alternatives. The concentration risk in Nvidia's CUDA ecosystem and TSMC's packaging capacity is driving this hedging strategy.
Google's TPU v7 demonstrates the performance gap custom silicon can achieve. For specific inference workloads, TPUs deliver better cost-per-inference than H100s. The catch: you're locked into that cloud's ecosystem. That's the point.
What this means for enterprise
If you're running AI workloads at scale, the hyperscaler you choose increasingly determines your silicon architecture. AWS Graviton instance types (C7g, R7g) offer 20-40% cost savings versus x86, but migration requires ARM compatibility testing. Graviton Windows support remains challenging.
The trade-off: better economics and tighter cloud integration versus reduced portability. For databases, analytics, and inference workloads, the numbers often justify migration. For training at frontier scale, you're still largely on Nvidia.
Three things to watch: Intel's 18A yield rates (AWS is betting on them), ARM software ecosystem maturity for cloud workloads, and whether custom ASICs can match Nvidia's pace on next-generation models. The moat is real, but it's being built around specific workloads, not general compute.
History suggests clouds will keep pushing custom silicon where economics work. The question for CTOs: which workloads justify the migration risk?