Short answers to the questions people ask before committing capital to industrial technology: pilots that stall, buying committees, budgets, agentic and physical AI. Each one points to the paper behind it and, where we have first-hand evidence, to the field note.
Where the full argument lives
An insight is the short answer. The papers carry the full argument and its sources, and the Field Notes record what we see running our own systems.
Which vendor sees your data when an AI agent calls a model?: Whichever one the routing layer picks. Every model call decides which provider sees which data, and a router that weighs only cost will eventually send regulated data somewhere convenient.
Is a spend cap enough to justify an agentic AI budget?: No. A cap stops a runaway bill, and a funding review asks which agent earned its spend. A team that cannot say loses the argument even when it stayed under budget.
Why an AI agent that acts needs different validation: An assistant that recommends and an agent that acts carry different risk, involve different approvers and clear different bars. Validate the level of autonomy, not only the use case.
What evidence a physical AI scale decision needs: Before leadership funds the next phase, three questions a lab result cannot answer have to be resolved: liability, the sim-to-real gap and operational continuity.
Why is product-market fit not enough in industry?: Industrial commercialization needs five fits at once. Product-market fit covers one, and initiatives fail on the four that standard validation never asks about.
Why do successful industrial pilots stall?: The pilot proved the technology works at one site. It never tested whether the buyers across the fleet will approve the spend, and that gap is where initiatives stop.
Who can stop an industrial deal?: Operations, IT and OT, safety, procurement, engineering, finance and a plant manager can each stop one, and each wants its own evidence.
The sim-to-real gap is a cost per site: Closing the gap between simulation and a real site has a price at every installation, and that price sets the unit economics.
Why interview frontline workers first, without managers?: A technician's or line operator's unprompted reaction is one of the strongest predictors of adoption at network scale, so an operator Sprint hears the frontline before management.
What makes a hypothesis falsifiable?: A hypothesis that cannot fail cannot be supported either. Before research starts, a Sprint fixes the hypothesis and the pass/fail criteria so no result can be reinterpreted afterward.