[Defense 2026] The 'Security Capitalism' Shift: Why Your Portfolio is Missing the Invisible Guardrail
The AI world is entering a new phase. Training chips — especially TPU-class accelerators — will determine which countries and companies lead the next wave of AI innovation.
AI models are doubling in size every few months.
GPU-based training has become too expensive, too slow, and too power-hungry.
Meanwhile:
TPUs deliver higher FLOPS per watt, lower cost per training run, and more stable scaling across large clusters.
This shift explains why major cloud regions (U.S., UAE, Korea, India) are adopting TPU-like accelerators in sovereign AI strategies.
| Category | GPU | TPU |
|---|---|---|
| Flexibility | Excellent | Medium |
| Training Throughput | Good | Superior |
| Cost Efficiency | Moderate | High |
| Best Use Case | Inference + general compute | Large-scale training |
The more parameters a model has,
the more likely TPUs outperform GPUs in both speed and cost.
Still leads TPU architecture, compiler, and pod-scale infrastructure.
Massively investing in sovereign AI compute with TPU-like efficiency chips.
Samsung & SK are preparing TPU-optimized HBM4 and LPDDR6 ecosystems.
They prefer TPUs because of lower capex, easier scaling, and reduced energy load.
The Stanford AI Index 2024–2025 confirms something important:
TPU-class accelerators reduced training cost per trillion parameters by over 60%.
This is one of the biggest turning points in AI economics.
👉 https://aiindex.stanford.edu/report/
(Stanford AI Index — 2024/2025 Official Report)
The AI race will not be won by who has the most GPUs —
but by who builds efficient, sovereign training infrastructure.
TPUs are becoming the backbone of that future.