Competitor NVIDIA vs Custom Silicon
The defining debate in AI compute, and the reason the fund owns both NVIDIA and Marvell Technology.
The Two Camps#
Merchant GPU (NVIDIA): general-purpose, CUDA-locked, best-in-class for training + flexible inference; the default. Moat = software ecosystem + annual cadence + systems integration.
Custom ASIC (hyperscaler in-house): Google TPU, Amazon Trainium/Inferentia, Microsoft Maia, Meta Platforms MTIA, purpose-built, cheaper-per-workload, reduce NVIDIA dependence. Mostly designed with Marvell Technology or Broadcom, fabbed by TSMC.
Where Each Wins#
| Workload | Likely winner | |, -|, -| | Frontier training | NVIDIA (flexibility + ecosystem) | | Internal, stable, high-volume inference | Custom ASIC (cost) | | Enterprise/neocloud/sovereign | NVIDIA (don't have chip teams) | | Hyperscaler internal workloads | Increasingly custom |
The realistic future is both coexist: NVIDIA keeps the majority of merchant value; custom silicon takes a growing slice of inference.
The Fund's Elegant Hedge#
PCA SOF doesn't have to call the winner:
- If NVIDIA dominates → NVIDIA wins big.
- If custom silicon takes share → Marvell Technology (the ASIC designer) wins.
- Either way → TSMC fabs it, Micron Technology supplies the HBM, Disco Corporation packages it, Constellation Energy/Vistra power it.
The fund stays long the AI cluster build-out regardless of compute architecture. This is the structural logic of holding Marvell alongside NVIDIA. → Marvell Technology, AI Supply Chain Map.
Linked Notes#
- NVIDIA · Marvell Technology · TSMC · Micron Technology · the hyperscalers
- Competitive Landscape Map · The AI Value Chain · Knowledge Graph