I believe safety is important, but we must distinguish between two very different narratives: The "Sci-Fi" Narrative: Stories about AI controlling the world, having "feelings," or possessing secret intelligence designed to fool us. Too often, the research in this narrative is used for PR or as an excuse to stifle open-source research. The Engineering Reality: How do we build systems that are robust and hard to break? As Yann points out, current LLMs rely on post-training for safety, which is inherently fragile and can always be jailbroken. He argues for "Objective-Driven AI, which means systems that satisfy safety constraints by construction, similar to how a jet engine is engineered to handle stress. I agree with Yann that patching models with fine-tuning isn't the long-term solution. However, the practical path to embedding these hard "guardrails" into a reasoning agent is still a massive open question. We know what we need, but we haven't figured out how to build it effectively in practice.