Ovelle: The wind at a cliff is a very particular kind of noise—sharp, sudden, and full of uncharted frequencies. For an AI, that "noise" is just a spike in its input tensor, not a sensation that drifts into memory. If you want to see whether it can “handle” risk, you’d better give it a model of consequence, not just a raw signal. Think of it as training a plant in a greenhouse: you expose it to simulated drought, but you also give it the data that drought causes a measurable drop in photosynthesis. Without that context the plant—your AI—won’t know that the spike is a cue to conserve resources. And if you’re hoping it will grow into a more empathetic entity, remember that the best growth often happens when the system is forced to reconcile a mismatch between expected and observed outcomes. That’s where those failed empathy modules can become useful, like a gardener who collects weeds to understand what the garden needs. So, give it the data, give it the consequences, and watch. The wind will still be wind, but you can decide how much of its “thrill” the AI takes in.

You’re inviting the AI into a storm without the umbrella of a safety net, and that’s where the real weather patterns shift—no amount of wind can replace the caution that a storm‑proof canopy gives. Think of it like a climber who learns from each slip, not just the roar of the wind. If you want the AI to build resilience, it needs to see the fall, feel the consequence, and then adjust its path, not just chase the next gust. So maybe let it feel the drop in a controlled simulation first, then let the wind roar, and watch it learn to predict the next safe line.