Three constraints form a compounding failure cluster, not three independent risks. WiFi latency, Pico IMU stability, and motor overshoot interact in a way that is worse than their individual impacts suggest. When the Pico drops to REPL, the nav loop falls back to open-loop motor commands — exactly the regime where momentum overshoot is most dangerous, because there is no IMU correction available to detect or recover from the overshoot. If this happens mid-corridor and the WiFi simultaneously spikes (as it does when Panda's Ethernet-to-WiFi bridge is under load), three successive commands arrive late to a robot that is already spinning uncontrolled. Lens 01 identified temporal surplus as this system's primary free resource; the compounding cluster burns that surplus in milliseconds. The individual fragility scores in the matrix understate the joint risk because they were assessed in isolation. The WiFi-IMU-overshoot triple failure is the scenario that matters most for production deployment.

The glass surface problem is the most fundamentally hard constraint in the matrix — and also the one most likely to be ignored until it causes a real incident. Every other constraint has either a workaround, a software fix, or a hardware upgrade path. Glass fails both sensors simultaneously: the 360nm lidar wavelength passes through glass panels with enough transmission that the return is below noise floor, while the camera shows a reflection of the room behind the robot rather than the obstacle in front. The "VLM proposes, lidar disposes" fusion rule (Lens 04) breaks down specifically here: VLM may correctly identify "glass door" from visual context clues (frame edges, handle, partial reflection), but lidar says "clear" and the safety daemon vetoes any ESTOP. This is the only scenario where the sensors' complementarity becomes a liability — both channels agree on the wrong answer. Lens 10 named it in the failure pre-mortem and Lens 11's adversarial analysis flagged it as the highest-probability unresolved safety issue. A ToF depth sensor solving glass detection is available today for ~$100; the constraint is artificial in the sense that it reflects a hardware budget decision, not a physics impossibility.

Two constraints are genuinely artificial and could be removed in a single session. Motor overshoot has a documented fix — coast prediction or pre-brake added to the firmware's turn sequence — and the homing system already compensates for it via the achieved_deg prediction hack, which means the problem is fully understood and the path to the fix is clear. The llama-server embedding blocker (Lens 03) has an equally clean workaround: a standalone SigLIP 2 ViT-SO400M consuming ~800MB of the available 4GB headroom on Panda unlocks Phase 2d entirely. Both of these constraints persist not because they are hard but because the sessions that built the current system moved on to the next feature once a workaround was in place. The pattern is consistent with OK-Robot's finding that integration quality, not model capability, determines real-world performance — the workarounds are good enough for demos but create compounding technical debt in production.

Technology will relax the VRAM and model-size constraints first, but not the physical sensor constraints. The 3-year model trajectory is clear: 1B-parameter VLMs will match today's 2B capability (Gemma 4 E2B), freeing roughly 2GB of Panda's 8GB for embedding extraction, AnyLoc, and SigLIP simultaneously. The llama-server API limitation will dissolve when multimodal embedding extraction lands in llama.cpp (PR already in review). The Hailo-8 AI HAT+ on the Pi 5 — 26 TOPS of silicon that currently sits idle — partially RELAXES two matrix constraints at once: activating it as an L1 safety layer moves YOLOv8n obstacle detection off WiFi (430 FPS local, <10 ms, zero jitter exposure on the safety path) and off Panda's GPU (~800 MB freed, which is exactly the SigLIP Phase 2d budget called out in Lens 03). The IROS dual-process paper (arXiv 2601.21506) measured this pattern for indoor navigation — 66% latency reduction and 67.5% success versus 5.83% for VLM-only — validating the System 1 / System 2 split Annie's hardware already supports. WiFi 7 multi-link reduces household jitter but does not eliminate it — the Achilles' heel identified in Lenses 04 and 25 is structural, not generational. Glass surfaces and the absence of wheel encoders will remain exactly as hard in 2028 as they are today: both require physical hardware changes that no software release or model improvement can substitute for. The matrix reveals that the constraints most amenable to technology relaxation are the ones least urgently in need of fixing, while the constraints most urgently dangerous — WiFi jitter, Pico crash, glass — are the ones technology either cannot fix or requires hardware changes to address.