Capacity-Planning

TL;DR for operators How do you scale a high-touch expert-support service when each central team can serve only so many locations and only within limited geographic reach? ARC tests an architecture in which central organizations seed nearby programs, while sufficiently mature supported sites can later become local providers themselves.1 In the paper’s sixth of twelve ordered assumption settings, central college-based hubs alone produce about 323 school programs after 40 years. Keeping the initial central hub while allowing supported schools to become local hubs raises that to about 551. Full ARC, which combines expansion of college hubs with recursive school-hub formation, reaches about 992. These are conditional simulation outputs, not forecasts. ...

A regional colocation provider redesigns its change-control workflow around six specialized agents that assemble evidence and coordinate reviews while engineers retain all production authority.

Opening — Why this matters now The first wave of enterprise AI adoption was obsessed with model choice. Which model is smarter? Which model writes better? Which model can reason, code, browse, call tools, summarize contracts, and politely pretend it enjoys quarterly planning? That was the easy part. The less glamorous question is now becoming more expensive: how do we serve all these model calls reliably, cheaply, and at scale? ...