Identify high-value workloads first: enterprise AI optimization before data is a mistake
matt_slotnick · x · 2026-09-25
mattslotnick argues that doing workload-specific model optimization upfront is usually unwise: you lack the data to fit models to real usage patterns.
- The real P0 is identifying the valuable workload and removing friction, since models can be swapped quickly and cheaply.
- Once workload shape is understood, well-resourced organizations with an owned intelligence initiative can post-train their own models to land better on the cost-perf curve.
Related event: Practitioners Advise Overprovisioning Frontier Models at AI Feature Launch(4 posts)→
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