The Great Flattening
TLDR
As AI inference costs decline, companies can process larger volumes of requests without hitting capacity constraints, shifting the focus from resource scarcity to deliberate prioritization of what to build. Organizations using this approach operate with smaller engineering teams while increasing API token consumption, enabling faster feature deployment. This allows product teams to respond directly to customer feedback with merged code changes rather than being bottlenecked by engineering capacity.
Why it matters
As intelligence costs drop, the backlog becomes less of a capacity problem and more of a choice, allowing founders to build and test many ideas rapidly. Companies adopting this approach operate with fewer engineers, larger token spend, faster shipping, and a direct line from customer needs to merged code.
