The research frames Phase 2 as a navigation improvement: more perception tasks per second, better obstacle awareness, richer commands. That framing is correct for the first order. But the second and third order tell a different story. The moment VLM scene classification reliably labels rooms at 10 Hz and attaches those labels to SLAM grid cells, Annie crosses a threshold that is not primarily technical. She stops being a robot that avoids walls and becomes a spatial witness — a household member with a persistent, queryable memory of where things are and what rooms look like. That transition changes the human relationship with the robot more than any hardware upgrade.

The crown jewel second-order effect is semantic map plus voice. It is not an obvious consequence of multi-query VLM — it emerges from the composition of three systems: SLAM provides the geometric scaffold, VLM scene classification provides the semantic labels, and the Context Engine provides the conversational memory that makes queries natural. None of these three subsystems was designed with "Annie, what's in the kitchen?" as a use-case. But the use-case falls out of their intersection as inevitably as electricity falls out of conduction. Mom will discover this naturally, without being told the feature exists. And the moment she discovers it, her model of Annie changes permanently: Annie is now someone who knows things, not just something that moves. (This is Lens 16's "build the map to remember" as lived experience, not research principle.)

The concerning third-order effect is trust exceeding capability. Phase 2c — semantic map annotation — is estimated at 65% probability of success. That means the map will be wrong 35% of the time about something. But families who have discovered that Annie can answer spatial queries will not maintain a probabilistic mental model of Annie's reliability. They will ask Annie where the glasses are, accept the answer, and occasionally be wrong. More troubling: they will ask Annie to adjudicate disagreements ("was the kitchen light on?"), and Annie's 65%-reliable answer will carry social weight in a family context. A wrong answer from a navigation system is a minor inconvenience. A wrong answer from a spatial witness is a domestic argument. The architecture must expose uncertainty — "I think I saw it on the nightstand, but I haven't been in there since 14:30" — or the trust gap will cause real friction.

The most leveraged second-order effect hiding in this research isn't in the VLM pipeline at all — it's in the idle 26 TOPS Hailo-8 NPU sitting unused on the Pi 5. Trace the chain: (1) activate Hailo for L1 obstacle detection at 430 FPS locally; (2) the safety path stops depending on WiFi, so 2-second brownout freezes disappear from the nav loop (Lens 20); (3) Mom stops flinching mid-task and her trust curve stabilises rather than dipping every few days; (4) she uses Annie more, which means more conversations, more room traversals, more labels accumulating on the SLAM grid; (5) the semantic map and Context Engine get richer faster, which reinforces the very use-cases (spatial queries, home historian) that make the trust sustainable. Five steps, each causally specific. And on the same activation, a parallel chain runs through the VRAM ceiling: Panda sheds the ~800 MB it was spending on obstacle inference, which is almost exactly the footprint SigLIP 2 needs for Phase 2d embedding extraction — so visual place memory and loop closure, which were architecturally blocked, become schedulable on hardware Annie already has. One idle hardware activation → three architectural gains: robust safety, accelerated trust, unblocked embedding memory. The IROS dual-process paper validates the latency story (66% reduction with fast-reactive + slow-semantic), but the lived benefit is larger than any single number: it's the cascade ratio. The counterweight — and this lens insists on naming it — is the new subsystem to maintain (HailoRT, TAPPAS, HEF compilation, firmware drift), which expands the 03:00 failure surface. Cascades are not free; they are worth their operational cost only if someone actually owns that cost.

Three steps downstream, the world being built here is one where the household's spatial memory is externalised into a machine. The family increasingly delegates the work of spatial recall ("where did I put X?", "what does the kitchen need?", "has anyone been in the study?") to Annie. This is qualitatively different from delegating physical tasks (vacuuming, fetching). Spatial memory is intimate — it is part of how people orient in their own homes. Outsourcing it to a robot with a camera, running 24 hours a day, is a profound restructuring of domestic privacy. The consent architecture, explicit data retention limits, and Mom's ability to say "don't record in the bedroom" are not privacy-law compliance tasks. They are the conditions under which the spatial witness role can be accepted rather than resisted. The ESTOP gap (Lens 21) is the acute safety risk; the surveillance drift is the chronic one. Both must be designed for before Phase 2c ships, not after.