@levie: Some good best practices here on AI token cost optimization. None of these happens though without a deep understanding …
Summary
A tweet thread discusses best practices for AI token cost optimization, arguing that a deep understanding of workflows and architecture is needed for enterprises to maximize ROI, and that this represents a major opportunity for applied AI companies.
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Cached at: 06/27/26, 08:01 PM
Some good best practices here on AI token cost optimization. None of these happens though without a deep understanding of the underlying work being done in a non-abstract way.
The ultimate implication is that a layer between the work itself and the underlying intelligence needs to deeply understand your workflows, context, and business process. Now, each individual company doing this on their own is unlikely to be effective at scale, so as a consequence, this is effectively the playbook for any applied AI company right now.
By evaling the models for the applied use cases, deeply understanding the domain, having tuned UX and features for the use case, and having the ability to support adoption and change (via FDEs), allow this layer to add a ton of value. And as a result, enterprises get higher ROI because you actually can get more intelligence per dollar by having optimal architecture and workflows.
There will be many horizontal and vertical versions of this approach. Huge opportunity right now.
Brian Armstrong (@brian_armstrong): How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching.
Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting
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