Uploaded August 2026 | Updated September 2026, 1 week ago
Ben Vallis from Meta presents his lighting talk on "The Plan That Learns" live at the Santa Clara Convention Center.
Network capacity planning is high-stakes and unforgiving: capacity is committed years ahead, every long-range forecast is guaranteed to be wrong, and the costliest surprises are correlated demand shocks that defeat traditional risk-pooling. Much of the pipeline is already automated — but the decisions that take real judgment have stayed manual, and those are the ones that decide whether a wrong forecast corners you.
This talk is about applying AI to exactly those decisions — not to chase a more accurate forecast, but to position capacity so it keeps our options open no matter how demand breaks. We show how agents are beginning to crack the three that matter most: acting inside narrow buy windows (triaging signals continuously to surface the handful of truly novel sites, weeks down to minutes), trusting data across systems that disagree on even basic definitions (a measure-enrich-regrade loop), and choosing among coupled, timing-sensitive levers (a ranked, costed menu of moves with the reasoning attached).
The real frontier is a plan that learns — agents that remember every decision and how it turned out, so human judgment compounds over time instead of resetting with each planner.
Learn more about @Scale here: atscaleconference.com
Ben Vallis from Meta presents his lighting talk on "The Plan That Learns" live at the Santa Clara Convention Center.
Network capacity planning is high-stakes and unforgiving: capacity is committed years ahead, every long-range forecast is guaranteed to be wrong, and the costliest surprises are correlated demand shocks that defeat traditional risk-pooling. Much of the pipeline is already automated — but the decisions that take real judgment have stayed manual, and those are the ones that decide whether a wrong forecast corners you.
This talk is about applying AI to exactly those decisions — not to chase a more accurate forecast, but to position capacity so it keeps our options open no matter how demand breaks. We show how agents are beginning to crack the three that matter most: acting inside narrow buy windows (triaging signals continuously to surface the handful of truly novel sites, weeks down to minutes), trusting data across systems that disagree on even basic definitions (a measure-enrich-regrade loop), and choosing among coupled, timing-sensitive levers (a ranked, costed menu of moves with the reasoning attached).
The real frontier is a plan that learns — agents that remember every decision and how it turned out, so human judgment compounds over time instead of resetting with each planner.
Learn more about @Scale here: atscaleconference.com
