If enterprise agents meaningfully increase governed data touchpoints, consumption can re-accelerate while value capture shifts up to policy, audit, and commerce rails. The main limiter is hyperscaler bundling and customer optimization, which can cap both growth and the terminal multiple.

As AI makes cognition cheap, the scarce asset is governed, auditable enterprise data access; Snowflake can re-accelerate consumption by becoming the default policy+permissions control plane for data-to-agent workloads, while adding higher-level value capture (commerce rails and outcome-priced decision products) to reduce pure “queries-to-dollars” fragility.

Positive alignment: AI/agents should increase always-on governed data access; Snowflake’s permissioning + sharing layer can become a tollgate, but hyperscalers as both suppliers and substitutes cap upside if bundling wins.

The non-linear upside is not “AI features,” it’s new workload categories that must touch governed data continuously (agent retrieval, evaluation/monitoring, audit trails, cross-org data sharing). Snowflake’s strongest path is to make its permissions/policy layer unavoidable even when execution engines and file formats get more interchangeable. That supports a multi-year consumption re-acceleration while the business remains capex-light (the binding constraint is hyperscaler economics, not data centers). I assume modest multiple compression as Snowflake scales, so the outcome is driven mainly by sustained revenue growth plus improved confidence in durability (governance + ecosystem), not by a speculative re-rate.

Two binding constraints dominate: (1) hyperscalers control the underlying unit-cost curve while also competing via bundling/credits, and (2) the consumption model is structurally exposed to customer optimization—Snowflake must continuously create net-new workloads (AI/agents, sharing, apps) faster than efficiency reduces credits consumed per outcome.

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