As builders, we are watching the industry race to scale bigger data centers into regions with available electricity, water, land, and natural resources. That concentration directly loads local ecosystems and the communities around them.
The technical issue is not AI as a category. The issue is how systems are architected and deployed. Too many workloads are being routed to the largest available LLMs when a smaller, specialized model - or even a deterministic pipeline - would deliver the same result with a fraction of the footprint.
Think of it like system architecture. You do not provision a cluster of high-end GPUs for a task that a CPU-optimized microservice can finish in milliseconds. Good engineering means matching the tool to the problem.
The competitive edge in AI is shifting. It is no longer about who can train the biggest model. It is about who can deliver the most useful inference per watt, per liter of water, and per kilogram of CO₂e. Efficient AI is the next engineering discipline.
