Reactie #1

The energy conversation is especially acute. Training a single large model can consume as much electricity as hundreds of homes use in a year, and inference at scale compounds that daily. The physical footprint grows faster than most procurement cycles can handle.

AI may be weightless, but try telling that to the finance team when the power bill arrives. Turns out "move fast and break things" hits differently when the things you're breaking are circuit breakers.

Infrastructure matters, but framing it as a choice between cloud, private, colocation, or hybrid assumes the workload is static. Most AI use cases evolve too quickly to lock into a foundation upfront - the real challenge is keeping infrastructure flexible enough to pivot as models and requirements change monthly.