BigQuery vs Warp

Warp has repositioned itself entirely around cloud software factories — automated SDLC loops driven by coding agents (triage, spec, implement, review, verify, ship, monitor). The two concrete products are Warp Factories (open, code-defined infrastructure for running these loops in the cloud) and the Warp Agent CLI (a standalone coding agent that works in any terminal, not just the Warp app). Factory Benchmarks, just launched, lets teams measure model and skill configurations against their own private codebase rather than synthetic benchmarks.

The sequence is deliberate: launch Factories as the infrastructure layer, launch the Agent CLI as the execution unit, then ship Benchmarks as the feedback mechanism that closes the improvement loop. The 'crawl, walk, run' adoption framing suggests Warp is in active go-to-market mode — the guides and thought-leadership posts are sales motion, not product changes. The next gap to fill is deeper observability into what the factory is actually doing at each stage.

The next concrete product move will likely be scheduling or orchestration tooling within Factories — the benchmarks surface tells you which configuration is best, but there's no way yet to trigger factory runs on a schedule or in response to events without re-configuring manually. CI trigger integration is the obvious next step.