The Burn Layer — Page 2

Failures become the operating condition at GPU cluster scale, not a rare risk to plan around.

Pre-revenue AI companies must design inference infrastructure before revenue arrives.

Financial models underestimate how fast AI hardware becomes obsolete.

Faster chips won't solve the bottleneck moving into memory systems.

GPU depreciation schedules encode infrastructure bets, not accounting conventions.

Hardware scarcity forces AI teams to optimize software rather than wait for more GPUs.

AI infrastructure now demands NVMe refreshes every two to three years, not five to seven.

How infrastructure teams read training benchmarks to spot system bottlenecks.

Hidden congestion management costs make RoCEv2 Ethernet expensive at scale.

How data path architecture determines whether GPUs train or idle during AI workloads.

Token-based billing masks the true cost driver: GPU memory consumed by the KV cache.