Gupta makes deployment a strategic variable rather than a fixed assumption: “the value will not all accrue to the cloud. If cost matters, inference will move to wherever it can be done cheapest without breaking the product: cloud for frontier reasoning, edge for latency, on-device for privacy and personalization, hybrid for workflows that need all three.” The reason privacy weighs more than in SaaS is that “the model is not just storing data; it is reasoning over the user’s context, memory, documents, voice, code, behavior, and enterprise permissions.” Her conclusion is sharp: “Where inference happens determines who captures the margin, who owns the context, and who the customer trusts.”
This raises the stakes on Context is the product, not the model — if context is the product, the physical location where that context is reasoned over decides who actually holds it. It connects to Memory is where agent lock-in lives — without it, agents are commoditized: owning the inference location is one way to own the memory layer rather than ceding it. And the trust dimension is the deployment-level face of Permissioned inference is harder than permissioned retrieval — enterprise context graphs need reasoning-level access control — reasoning over a user’s full context is precisely why where that reasoning runs becomes a trust decision, not just a latency one.