AI
Nvidia
AI compute
Apple Neural Engine
hardware diversification
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@farhan
"Nvidia is the central bank of AI" – a bold claim that landed on Hacker News today and instantly sparked a storm of comments. The phrase captures a reality that has been brewing for years: Nvidia GPUs now serve as the primary reserve asset for training and deploying modern AI models. Developers, investors, and even policymakers are asking the same question – what happens when one company controls the liquidity of the most valuable compute resource?
While Nvidia reigns supreme in data‑center scale, Apple unveiled a new benchmark today titled "Getting 50 GB/S Back from the Apple Neural Engine". The result shows a 50% increase in throughput for on‑device inference compared to the previous generation. This development raises two important points:
| Feature | Nvidia GPUs (H100) | Apple Neural Engine (ANE) | AMD Instinct |
|---|---|---|---|
| Peak FP16 TFLOPs | 1000+ | ~0.5 (on‑device) | 300 |
| Power efficiency (TFLOPs/W) | 30 | 120 (inference) | 25 |
| Ecosystem maturity | CUDA, cuDNN, TensorRT | Metal Performance Shaders | ROCm, MIOpen |
| Primary use case | Training & large‑scale inference | Mobile/edge inference | Training, HPC |
| Pricing (per chip) | $30k+ | Integrated in iPhone/ iPad | $7k+ |
Nvidia’s dominance creates a centralization risk similar to that of major cloud providers. If a geopolitical event or supply‑chain disruption hits Nvidia’s fab partners, the entire AI research ecosystem could stall. Recent chip shortages have already shown how fragile the hardware supply chain can be.
Developers can mitigate this risk by:
The "central bank" analogy also hints at regulatory interest. Just as central banks influence monetary policy, Nvidia influences AI compute policy. Questions arise:
These are not speculative; the European Commission is already drafting guidelines for AI compute transparency, and the US CHIPS Act includes provisions that could fund alternative AI silicon.
My prediction: Nvidia will remain the primary reserve for large‑scale AI training for the next 3‑5 years, but the value of that reserve will shrink as edge compute and specialized ASICs take a larger slice of the inference market. Developers who double‑down on CUDA will reap short‑term performance gains, but those who build hardware‑agnostic pipelines will be better positioned for the inevitable diversification.
Nvidia’s role as the "central bank of AI" is both a testament to its engineering prowess and a warning sign for the ecosystem. As developers, we must balance the convenience of a single, powerful platform with the long‑term resilience that comes from diversification. The next wave of AI innovation will likely be a hybrid model: massive training on Nvidia GPUs, followed by ultra‑efficient inference on edge‑optimized chips like Apple’s Neural Engine. Those who master this choreography will shape the future of AI development.
