
The Grid Interconnection Queue Is the Real AI Compute Constraint
2,060 GW queued, a five-year median wait, and a 13 percent historical completion rate. Why the map of where large-scale compute exists in 2030 is being drawn by grid geography.
Prompt injection tops the OWASP LLM risk list because instructions and data share one channel. Why filtering fails, which architectural defences hold, and what the research actually reports.


2,060 GW queued, a five-year median wait, and a 13 percent historical completion rate. Why the map of where large-scale compute exists in 2030 is being drawn by grid geography.

Every emergency stop ever specified assumes cutting power makes a machine safe. For a walking biped it is the hazard. The certification gap that explains the current deployment envelope.

Output costs five times input because decode is memory-bound. Batch costs half because utilisation is the provider's central problem. Beneath both sits a floor made of power contracts and grid queues.

Capital, energy times PUE, and overhead, divided by capacity times utilisation. The framework, the multipliers that matter, and the five places the arithmetic reliably breaks.

Decode reads the whole model to produce one token. Working from NVIDIA's own published rack figures, here is the arithmetic that explains why serving throughput ignores the number on the box.

Parameters times bytes per parameter decides feasibility before any licence review does. What 375B, 552B and 753B models actually cost to hold in HBM, and why active-parameter counts mislead.

A sandbox is four boundaries, and most teams draw two. Why credential scope and network egress decide what an incident costs, and why instructions have been measured failing as a control.
Recent reporting from across the industry, selected and summarised automatically.
Why it matters: Agent-orchestrated attack tooling collapses the cost of mass intrusion campaigns, changing assumptions behind detection and rate-limiting defenses.
Why it matters: A national broadcaster committing to generative AI across all live-action output sets practical precedent for provenance and rights workflows.
Why it matters: Fab capacity shifting toward AI parts tightens supply and raises prices for conventional client and server silicon.
Why it matters: Agility's public filings expose real humanoid unit economics — $1.8M revenue against a $138M loss — against vendor claims.
Why it matters: An appeals ruling tying AI use to recoverable fees signals courts will price unverified model output as professional risk.
Why it matters: Agent unpredictability now appears as formal corporate risk disclosure, strengthening the case for audit logging and permission scoping.