How AI Chips Shape the Future of Computing
Compute is the budget, the bottleneck, and the moat.
AI accelerators shape the entire stack above them. Model size, training time, inference cost, and product economics all bend to what the chip allows.
Who supplies the chips
NVIDIA dominates training with the H100 and B200 generations. Google's TPUs power a large share of internal Google workloads. AMD's MI300 line is gaining ground. Custom silicon from Amazon (Trainium, Inferentia), Microsoft (Maia), and Meta (MTIA) targets internal cost reduction.
Why it matters beyond hardware
The chip layer dictates which model architectures get explored. Memory bandwidth, interconnect, and software toolchains push researchers toward what runs well, not just what is theoretically interesting.
What to watch
- Inference costs per million tokens, not training cost.
- Power per accelerator and data centre power availability.
- Toolchain maturity (CUDA versus alternatives).
- Government export controls and supply concentration.
The bigger signal
Compute is now a geopolitical asset. Watch grid capacity, water access, and trade policy as closely as benchmark scores.
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